Detecting and validating a detection of a state change from a template of heart rate derivative shape or heart beat wave complex

ABSTRACT

Methods, systems, and apparatus for detecting and/or validating a detection of a state change by matching the shape of one or more of an cardiac data series, a heart rate variability data series, or at least a portion of a heart beat complex, derived from cardiac data, to an appropriate template.

This application is a continuation-in-part of prior co-pendingapplication Ser. No. 12/884,051, filed Sep. 16, 2010.

1. FIELD OF THE INVENTION

This invention relates to medical device systems and methods capable ofdetecting or validating a detection and, in some embodiments, treatingan occurring or impending state change.

2. DESCRIPTION OF THE RELATED ART

Approximately 60 million people worldwide are affected with epilepsy, ofwhom roughly 23 million suffer from epilepsy resistant to multiplemedications. In the USA alone, the annual cost of epilepsy care is USD12 billion (in 1995 dollars), most of which is attributable to subjectswith pharmaco-resistant state changes. Pharmaco-resistant state changesare associated with an increase mortality and morbidity (compared to thegeneral population and to epileptics whose state changes are controlledby medications) and with markedly degraded quality of life for patients.State changes may impair motor control, responsiveness to a wide classof stimuli, and other cognitive functions. The sudden onset of apatient's impairment of motor control, responsiveness, and othercognitive functions precludes the performance of necessary and evensimple daily life tasks such as driving a vehicle, cooking, or operatingmachinery, as well as more complex tasks such as acquiring knowledge andsocializing.

Therapies using electrical currents or fields to provide a therapy to apatient (electrotherapy) are beneficial for certain neurologicaldisorders, such as epilepsy. Implantable medical devices have beeneffectively used to deliver therapeutic electrical stimulation tovarious portions of the human body (e.g., the vagus nerve) for treatingepilepsy. As used herein, “stimulation,” “neurostimulation,”“stimulation signal.” “therapeutic signal,” or “neurostimulation signal”refers to the direct or indirect application of an electrical,mechanical, magnetic, electro-magnetic, photonic, acoustic, cognitive,and/or chemical signal to an organ or a neural structure in thepatient's body. The signal is an exogenous signal that is distinct fromthe endogenous electro-chemical, activity inherent to the patient's bodyand also from that found in the environment. In other words, thestimulation signal (whether electrical, mechanical, magnetic,electro-magnetic, photonic, acoustic, cognitive, and/or chemical innature) applied to a cranial nerve or to other nervous tissue structurein the present invention is a signal applied from a medical device,e.g., a neurostimulator.

A “therapeutic signal” refers to a stimulation signal delivered to apatient's body with the intent of treating a medical condition through asuppressing (blocking) or modulating effect to neural tissue. The effectof a stimulation signal on neuronal activity may be suppressing ormodulating; however, for simplicity, the terms “stimulating”,suppressing, and modulating, and variants thereof, are sometimes usedinterchangeably herein. In general, however, the delivery of anexogenous signal itself refers to “stimulation” of an organ or a neuralstructure, while the effects of that signal, if any, on the electricalactivity of the neural structure are properly referred to as suppressionor modulation.

Depending upon myriad factors such as the history (recent and distant)of the nervous system, stimulation parameters and time of day, to name afew, the effects of stimulation upon the neural tissue may be excitatoryor inhibitory, facilitatory or disfacilitatory and may suppress,enhance, or leave unaltered neuronal activity. For example, thesuppressing effect of a stimulation signal on neural tissue wouldmanifest as the blockage of abnormal activity (e.g., epileptic statechanges) see Osorio et al., Ann Neurol 2005; Osorio & Frei IJNS 2009)The mechanisms thorough which this suppressing effect takes place aredescribed in the foregeoing articles. Suppression of abnormal neuralactivity is generally a threshold or suprathreshold process and thetemporal scale over which it occurs is usually in the order of tens orhundreds of milliseconds. Modulation of abnormal or undesirable neuralactivity is typically a “sub-threshold” process in the spatio-temporaldomain that may summate and result under certain conditions, inthreshold or suprathreshold neural events. The temporal scale ofmodulation is usually longer than that of suppression, encompassingseconds to hours, even months. In addition to inhibition ordysfacilitation, modification of neural activity (wave annihilation) maybe exerted through collision with identical, similar or dissimilarwaves, a concept borrowed from wave mechanics, or through phaseresetting (Winfree).

In some cases, electrotherapy may be provided by implanting anelectrical device, i.e., an implantable medical device (IMD), inside apatient's body for stimulation of a nervous tissue, such as a cranialnerve. Generally, electrotherapy signals that suppress or modulateneural activity are delivered by the IMD via one or more leads. Whenapplicable, the leads generally terminate at their distal ends in one ormore electrodes, and the electrodes, in turn, are coupled to a targettissue in the patient's body. For example, a number of electrodes may beattached to various points of a nerve or other tissue inside a humanbody for delivery of a neurostimulation signal.

Although non-contingent, programmed periodic stimulation (also referredto as “open-loop,” “passive,” or “non-feedback” stimulation (i.e.,electrotherapy applied without reference to sensed information)) is theprevailing modality, contingent (also referred to as “closed-loop,”“active,” or “feedback” stimulation (i.e., electrotherapy applied inresponse to sensed information)) stimulation schemes have been proposed.Included in such proposed stimulation schemes are electrotherapy appliedin response to an indication of an impending, occurring, or occurredstate change, with the intent of reducing the duration, the severity, orboth of a state change or a post-state change recovery period. However,such stimulation schemes would require reasonably sensitive techniquesfor indicating an impending, occurring, or occurred state change.

Even if closed-loop neurostimulation, or any other therapy for epilepsy,is not performed, reasonably sensitive and/or specific techniques forindicating an impending, occurring, or occurred state change would bedesirable for warning of state changes to minimize risk of injuries andfor logging to assess the state of the disease and assess the efficacyof therapies. Numerous studies have shown that self-reporting bypatients, such as in state change diaries, generally only captures abouthalf of all state changes having both electroencephalographic (EEG) andclinical signatures. Roughly a third of all patients do not identify anyof their state changes. Detection of brain state changes may beaccomplished using different body signals, but cortical electricalsignals are most commonly used for this purpose. For multiple reasons(e.g. signal to noise ratio, stability of signals, etc.) intracranialand not scalp recordings are the modality of choice for prolonged (e.g.weeks to years) recording of cortical signals. However, since use ofintracranial signals requires costly and burdensome surgical proceduresthat are associated with certain potentially serious complications, theyare neither accessible nor acceptable to the majority of hundreds ofthousands of patients that could benefit from them. Use of non-cerebralor extra-cerebral signals has emerged as a viable, useful, and highlycost-effective alternative to electrical cortical signals for thedetection, warning, and logging of brain state changes, such asepileptic seizures.

SUMMARY OF THE INVENTION

In one aspect of the present invention, a method for indicating anoccurrence of a state change is provided. In one aspect of the presentinvention, the method comprises obtaining a time series of cardiac datafrom a patient; determining a reference heart rate parameter from saidcardiac data; determining a heart rate derivative shape from said timeseries of cardiac data, wherein said heart rate derivative shapecomprises at least one characteristic selected from a number of phasesrelative to said reference heart rate parameter, a number of extrema ofsaid heart rate derivative, a number of directions of change of saidheart rate derivative, an area under the curve of at least one phase, anumber of positive phases, or a number of negative phases; andindicating an occurrence of a state change based upon a determinationthat said heart rate derivative shape matches a state change template insaid at least one characteristic, wherein said at least onecharacteristic of said state change template comprises two or morephases relative to said reference heart rate parameter, two or moreextrema of said heart rate derivative, three or more directions ofchange of said heart rate derivative, a number of positive phases, or anumber of negative phases, provided the total number of positive phasesand negative phases is two or more.

In another aspect of the present invention, a method for indicating anoccurrence of a state change is provided. In one aspect of the presentinvention, the method comprises obtaining data relating to at least aportion of a heart beat complex from a patient; comparing said at leastsaid portion of said heart beat complex with a corresponding portion ofa reference heart beat complex template of said patient; and indicatingan occurrence of a state change based upon a determination that saidheart beat complex fails to match said reference heart beat complextemplate.

In yet another aspect of the present invention, a computer readableprogram storage device is provided that is encoded with instructionsthat, when executed by a computer, perform a method described above.

In one aspect of the present invention, a medical device is providedcomprising a computer readable program storage device as describedabove.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 provides a stylized diagram of a medical device implanted into apatient's body for providing a therapeutic electrical signal to a neuralstructure of the patient's body, in accordance with one illustrativeembodiment of the present invention;

FIG. 2A is a block diagram of a medical device system that includes amedical device and an external unit, in accordance with one illustrativeembodiment of the present invention;

FIG. 2B is a block diagram of a medical device system that includes amedical device and an external unit, in accordance with one illustrativeembodiment of the present invention;

FIG. 3A is a stylized block diagram of a cardiac data collection moduleof a medical device, in accordance with one illustrative embodiment ofthe present invention;

FIG. 3B is a stylized block diagram of an heart beat/intervaldetermination module of a medical device, in accordance with oneillustrative embodiment of the present invention;

FIG. 3C is a stylized block diagram of a HR derivative/complex module ofa medical device, in accordance with one illustrative embodiment of thepresent invention;

FIG. 3D is a stylized block diagram of a template match module of amedical device, in accordance with one illustrative embodiment of thepresent invention;

FIG. 4 illustrates a flowchart depiction of a method for detecting astate change and taking one or more responsive actions, in accordancewith an illustrative embodiment of the present invention;

FIG. 5 shows basic shapes of a heart rate plot, from which more complexshapes can be produced by deformation in accordance with an illustrativeembodiment of the present invention;

FIG. 6 shows a graph of heart rate (BPM) vs. time (hr), with anepileptic event identified by electrocorticography (ECoG) indicated byvertical lines, from which a triangle pattern is discernible, inaccordance with an illustrative embodiment of the present invention;

FIG. 7A-C shows three graphs of heart rate vs. time, with epilepticevents identified by ECoG indicated by vertical lines, from each ofwhich a notched triangle pattern is discernible, in accordance with anillustrative embodiment of the present invention;

FIG. 8A-C shows three graphs of heart rate vs. time, with epilepticevents identified by ECoG indicated by vertical lines, from each ofwhich an “M” pattern is discernible, in accordance with an illustrativeembodiment of the present invention;

FIG. 9 shows a graph of heart rate vs. time, with an epileptic eventidentified by ECoG indicated by vertical lines, from which a “W” patternis discernible, in accordance with an illustrative embodiment of thepresent invention;

FIG. 10 shows a graph of heart rate vs. time, with an epileptic eventidentified by ECoG indicated by vertical lines, from which a fused “M”and “W” pattern is discernible, in accordance with an illustrativeembodiment of the present invention;

FIG. 11A-B shows two graphs of heart rate vs. time, with epilepticevents identified by ECoG indicated by vertical lines, from which apattern of periodic oscillations is discernible, in accordance with anillustrative embodiment of the present invention;

FIG. 12 shows a graph of heart rate vs. time, with an epileptic eventidentified by ECoG indicated by vertical lines, from which a pattern ofperiodic oscillations, specifically forming a sawtooth pattern, isdiscernible, in accordance with an illustrative embodiment of thepresent invention;

FIG. 13A-D shows four graphs of heart rate vs. time, with epilepticevents identified by ECoG indicated by vertical lines, from which apattern of periodic oscillations overlaid on a longer-timescale trianglepattern is discernible, in accordance with an illustrative embodiment ofthe present invention;

FIG. 14 shows a graph of heart rate vs. time, with an epileptic eventidentified by ECoG indicated by vertical lines, from which periodicoscillations forming a “comb” pattern are discernible, as well as apattern of lower amplitude periodic oscillations overlaid on alonger-timescale triangle pattern is discernible, in accordance with anillustrative embodiment of the present invention;

FIG. 15 shows a graph of heart rate vs. time, with an epileptic eventidentified by ECoG indicated by vertical lines, from which a pattern ofperiodic oscillations overlaid on a longer-timescale parabola pattern isdiscernible, in accordance with an illustrative embodiment of thepresent invention;

FIG. 16 shows a graph of heart rate vs. time, with an epileptic eventidentified by ECoG indicated by vertical lines, from which a triphasicpattern is discernible, in accordance with an illustrative embodiment ofthe present invention;

FIG. 17A-B shows two graphs of heart rate vs. time, with epilepticevents identified by ECoG indicated by vertical lines, from whichmultiple “M” and/or “W” patterns are discernible, in accordance with anillustrative embodiment of the present invention;

FIG. 18 shows exemplary heart beat complex changes detectable by use ofthe P wave and the R wave of a heart beat, in accordance with anillustrative embodiment of the present invention; and

FIG. 19 shows a first heart beat complex derived from data collectedover an entire period of EKG monitoring of a patient (A) and a secondheart beat complex derived from EKG data collected from the same patientduring circumictal periods only (B).

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Illustrative embodiments of the invention are described herein. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. In the development of any such actualembodiment, numerous implementation-specific decisions must be made toachieve the design-specific goals, which will vary from oneimplementation to another. It will be appreciated that such adevelopment effort, while possibly complex and time-consuming, wouldnevertheless be a routine undertaking for persons of ordinary skill inthe art having the benefit of this disclosure.

This document does not intend to distinguish between components thatdiffer in name but not function. In the following discussion and in theclaims, the terms “including” and “includes” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to.” Also, the term “couple” or “couples” is intended to meaneither a direct or an indirect electrical connection. “Direct contact,”“direct attachment,” or providing a “direct coupling” indicates that asurface of a first element contacts the surface of a second element withno substantial attenuating medium there between. The presence of smallquantities of substances, such as bodily fluids, that do notsubstantially attenuate electrical connections does not vitiate directcontact. The word “or” is used in the inclusive sense (i.e., “and/or”)unless a specific use to the contrary is explicitly stated.

The term “electrode” or “electrodes” described herein may refer to oneor more stimulation electrodes (i.e. electrodes for delivering atherapeutic signal generated by an IMD to a tissue), sensing electrodes(i.e., electrodes for sensing a physiological indication of a state of apatient's body), and/or electrodes that are capable of delivering atherapeutic signal, as well as performing a sensing function.

In one embodiment, the present invention provides a method of detectinga state change based upon data derivable from cardiac signals. The statechange can be, for example, at least one of an unstable brain state, abrain state indicative of an elevated probability of a state change, abrain state indicative of an impending state change, or a state change,among others.

In one embodiment, the present invention provides a method forindicating an occurrence of a state change. In one embodiment, themethod comprises obtaining a time series of cardiac data from a patient;determining a reference heart rate parameter from said cardiac data;determining a heart rate derivative shape from said time series ofcardiac data, wherein said heart rate derivative shape comprises atleast one characteristic selected from a number of phases relative tosaid reference heart rate parameter, a number of extrema of said heartrate derivative, a number of directions of change of said heart ratederivative, a number of positive phases, or a number of negative phases;and indicating an occurrence of a state change based upon adetermination that said heart rate derivative shape matches a statechange template in said at least one characteristic.

The cardiac data can be gathered by any of a number of techniques. Forexample, the cardiac data may be gathered by an electrocardiogram (EKG)device. For another example, the cardiac data may be gathered by acranial nerve stimulator device. In one embodiment, the cardiac data maybe related to the R-waves of the beat sequence, such as a time series ofR-waves or a series of R-R intervals. Those skilled in the art havingbenefit of the present disclosure would appreciate that other timeseries of cardiac waves and/or their fiducial points (e.g., P waves, Twaves, etc.) may be used and still remain within the spirit and scope ofthe present invention.

Data relating to R-waves may be gathered by an EKG device or, in oneembodiment, by a vagus nerve stimulator, such as described in U.S. Pat.No. 5,928,272, which is hereby incorporated by reference herein.

Obtaining the cardiac data may comprise sensing a time of beat sequenceof a patient's heart and generating a time series data stream from thetime of the beat sequence. In a further embodiment, receiving thecardiac data of the patient's heart may comprise sensing andtime-stamping a plurality of R waves, and generating the time seriesdata stream may comprise determining a series of R-R intervals from thetime stamps of the sensed R waves.

In one embodiment, the fiducial time marker is an R wave peak orthreshold crossing. The amplitude or height of one or morerepresentative R waves may be used to set a threshold that, when reachedor crossed, is registered as a fiducial time marker of a heart beat.

In one embodiment, a heart rate derivative is determined from the timeseries of cardiac data. As defined herein, a “heart rate derivative” isa value derivable, directly or indirectly, from the time series ofcardiac data, wherein the value relates to a feature, property orrelationship between two or more heart beats. Although a first orhigher-order derivative, as understood from calculus, is a “heart ratederivative” under the above definition, a heart rate derivative is notnecessarily a first or higher-order calculus derivative. Exemplary heartrate derivatives include, but are not limited to, heart rate and heartrate variability (HRV). A “shape” is used herein to refer to a featureapparent to the person of ordinary skill in the art upon viewing a graphof the heart rate or of one of its derivative over a period of time. Inone embodiment, a heart rate derivative shape comprises at least onecharacteristic selected from a number of phases relative to a referenceheart rate parameter, a number of extrema of the heart rate derivative,a number of directions of change of the heart rate derivative, a numberof positive phases, or a number of negative phases.

By “heart rate shape” is meant one or more characteristics or featuresof a time series of cardiac data that are reflective of the appearanceof that time series if plotted on a graph (on the y-axis and time on thex-axis). For example, one characteristic of heart rate shape is a numberof phases relative to the reference heart rate parameter. A “phase” is aperiod between two consecutive deviations from, crossings of, or returnsto the reference heart rate parameter. A phase may be positive (having avalue greater than the reference heart rate parameter) or negative(having a value less than the reference heart rate parameter). Yetanother exemplary characteristic of heart rate shape is a number ofextrema of heart rate. An “extremum” (plural, “extrema”) is a pointwhere the slope of heart rate changes sign, or phrased alternatively, apoint that is a highest high or lowest low of heart rate for some lengthof time or number of beats before and after. Still another exemplarycharacteristic of heart rate shape is a number of directions of heartrate change, which can be defined as the number of changes of the signof the slope of heart rate, plus one. Yet another exemplarycharacteristic of heart rate shape is the steepness of one or moreascending or descending slopes.

Though not to be bound by theory, we have found that heart activityduring normal states (exercise, anger, etc.) and abnormal states (e.g.,epileptic seizures) as displayed or graphed over various time scalestake on distinctive shapes which may be used to identify the variousstates as well as changes from one state to another, such as fromnon-seizure to seizure. Said shapes are considered and treated herein astemplates, given their stereotypical nature, and are used in severalways (to be described below) to detect states, state changes, stateand/or state change onsets, and/or other features, such as duration,intensity or magnitude, and/or other relevant characteristics, such astype of state or state change.

Another heart rate derivative that may be considered is a heart ratevolatility (non-stationarity) parameter, a measure of dispersion whichmay be defined as a change in the standard deviation or variance ofheart rate over a moving window. Commonly, the higher the volatility,the higher appears to be the probability of state changes. Volatilty, ametric often found in financial contexts, is used here to obtain certaininformation about the state of a system regardless of the similaritiesor dissimilarities between financial and biological time series andconsideration for the underlying systems' dynamics.

For example, let . . . ^(t−2)

, t^(t−1)

, ^(t+1)

, . . . be a stochastic process. Its terms ^(t)

represent heart rates as components of a vector or a matrix. Thevolatility of the process at time t⁻¹ is defined as the standarddeviation of the time t return. Typically, log returns are used, so thedefinition becomes

$\begin{matrix}{{{volatility} = {{std}\left( {\log \left( \frac{\,^{t}Q}{\,^{t - 1}Q} \right)} \right)}}} & \lbrack 1\rbrack\end{matrix}$

where log denotes a natural logarithm.

If heart rate time series are conditionally homoskedastic, definition[1] is precise. However, if they are conditionally heteroskedastic,measure [1] requires modification. Volatility at time t⁻¹ represents inthis case, the standard deviation of the time t log return conditionalon information available at time t⁻¹ as defined below

${{volatility} = {{\,^{t - 1}{std}}\left( {\log \left( \frac{\,^{t}Q}{\,^{t - 1}Q} \right)} \right)}}$

where the preceding superscript t⁻¹ indicates that the standarddeviation is conditional on information available at time t⁻¹.

Transitions from homoskedasticity (defined herein as approximatelyconstant standard deviations over a certain time window) toheteroskedasticity (inconstant standard deviation) also provideinformation about the probability of being in or near a state change ofinterest and may be used for automated detection, warning, delivery oftherapy and logging (of events, warnings and therapy) purposes.

Volatility will be measured using time scales (seconds to days) based ontemporal (e.g., duration) and other properties of the state change oninterest and of the reference state.

The method also comprises indicating an occurrence of a state changebased upon a determination that said heart rate derivative shape matchesa state change template in said at least one characteristic.

A “state change template” is a template known or discovered by thepractitioner to be associated with the state change, wherein thetemplate can be used in the analysis of the heart rate derivative shape.

Plots of instantaneous heart rate (y-axis) as a function of time(x-axis) in subjects with epilepsy reveal consistent changes before,during and after seizures, referred herein to as circum-ictal changes.(“Circum-ictal” or “circumictal,” as used herein, encompasses preictal,ictal, and post-ictal subperiods. The circumictal period can beconsidered the time window (e.g., in min) preceding and following aseizure during which cardiac activity differs from that observed duringinterictal conditions, normal physical activity (including exercise),intense emotions (fear, anger, etc.), and physiological functions suchas defecation, urination or coitus). The curves described by thesecircum-ictal changes in heart rate, approximate triangles or parabolae,and may have indentations of varying sizes. See the discussion of FIGS.5-17 below for more information. Visual review of a large human databaseof instantaneous heart rate plots reveal that over a certain windowlength (referred herein as the mesoscopic scale) their circum-ictalshapes are limited to the triangles and parabolae and to “deformations”of these two shapes (see FIG. 5). These “deformations” appear to havetemporal and magnitude dependencies, in that the longer the duration ofthe change in heart rate and the larger its magnitude, the more likelythey are to occur. The behavior of these shapes likely reflectfluctuations in the strength of sympathetic and parasympathetic inputsto the heart. For example, transient, rapid drops in heart rate may becaused by either a withdrawal in sympathetic tone or by an increase inparasympathetic tone resulting from differential activation orinhibition by epileptiform activity of brain regions involved inautonomic control.

The shape (i.e., all the geometrical information that is invariant toposition (including rotation) and scale) of these curves may be used fordetection of changes in brain state such as epileptic seizures and theirproperties may be characterized through use of statistical shapeanalysis (e.g., Procrustes analysis), of the different embodiments of“matched filtering” or of other geometrical (Euclidian andnon-Euclidian) methods. Other approaches such as computing the area ofthe triangles and parabolae and comparing the results to a referencevalue outside the circum-ictal state, may be used. In the case oftriangles, there area may be calculated using for example Heron'sformula:

Area=√{square root over (S(S−a)(S−b)(S−c))}{square root over(S(S−a)(S−b)(S−c))}{square root over (S(S−a)(S−b)(S−c))}, where

S=½(a+b+c) and a, b, and c are the sides of the triangle. Similarly thearea of parabolae (Area=⅔ b×h, where b is the base and the height, maybe computed and used to detect seizures.

Other attributes not captured by the concept of shape may be applied asneed to the sign al for detecting state changes such as epilepticseizures.

In one embodiment, the at least one characteristic of the state changetemplate comprises two or more phases relative to the reference heartrate parameter, two or more extrema of the heart rate derivative, threeor more directions of change of the heart rate derivative or its slope,a number of positive phases, or a number of negative phases, providedthe total number of positive phases and negative phases is two or more.

In another embodiment, the at least one characteristic of the statechange template comprises at least one phase relative to the referenceheart rate parameter, at least one extremum of the heart rate derivativeor its slope, two or more directions of change of the heart ratederivative, a number of positive phases, or a number of negative phases,provided the total number of positive phases and negative phases is atleast one.

In another embodiment, the at least one characteristic of the statechange template comprises at least one of the amplitude of at least onephase, the duration of at least one phase, the valence (positive ornegative) of at least one phase, at least one slope of at least onephase, the arc length (which is used interchangeably with line length)of at least one phase, the number of extrema in at least one phase, andthe sharpness of the extrema of at least one phase.

A reference heart rate parameter, as used herein, is a reference valueobtained during a state that is deemed of no or little interest forautomated detection, warning, treatment or logging purposes. Thereference heart rate parameter may be a single value, a series ofvalues, a statistical is selected from the group consisting of a shape,a vector, a vector space, a matrix, and two or more thereof.

For example, heart activity during a non-seizure state is considered asa reference state. The reference heart rate parameter may be calculatedfrom a time series of value over any particular window, such as a windowhaving a length from 30 sec to 24 hr, although longer or shorter windowsmay be used. The window may be a simple window or anexponentially-forgetting window, as discussed in U.S. patent applicationSer. Nos. 12/770,562, filed Apr. 29, 2010; 12/771,727, filed Apr. 30,2010; and 12/771,783, filed Apr. 30, 2010; the disclosures of eachhereby incorporated herein by reference. The reference heart rateparameter may be calculated as any measure of any tendency of the timeseries, such as the central tendency of the time series. For example,the reference heart rate parameter may be calculated as a mean, median,nth percentile (where n can be from 30 to 70), or exponential movingaverage of the time series, among other measures of central tendency.Other mathematical or statistical measures, including, but not limitedto, correlation dimension, entropy, Lyapunov exponents, and fractal ormultifractal dimensions, may be also applied to any of the parameters ortheir templates.

The reference heart rate parameter may be determined from previouslyrecorded data, or from “normative” values obtained from normal orabnormal cohorts of subjects or populations or it may be determined fromthe time series of cardiac data referred to above.

An exemplary state change template can be derived from the pattern shownin FIG. 8, wherein the changes in heart rate during a seizure form areadily discernible “M” between 0.88 hr and 0.92 hr, having one positivephase relative to a reference heart rate parameter (calculated as themedian value from about 0.85 hr to 0.89 hr and from about 0.93 hr toabout 1.00 hr), three extrema (two maxima and one minimum, each being anextremum relative to about 20 seconds before and 20 seconds after), andfour directions of heart rate change.

The state change template may be the “raw” pattern (analog or digitized)or it can be derived by smoothing, averaging, or otherwisemathematically processing subseries of cardiac data obtained duringstate changes. A “matched filter” is a type of filter matched to theknown or assumed characteristics of a target signal, to optimize thedetection of that signal in the presence of noise. A matched filter isthe filter with impulse response equal to the time reversed, complexconjugate impulse response of the input.

One skilled in the art will appreciate that when applying matched filtertechniques to attempt to detect a pattern in a signal, the raw signalmay first be transformed so that it has zero mean on a timescale ofinterest when the pattern is absent. Such transformation may include,but not be limited to, detrending or subtracting a background referencevalue (or time-varying reference signal) from the raw signal and is usedto remove bias in the matched filter output and improve itssignal-to-noise ratio.

Seizure detection may be performed over multiple time scales or windowlengths listed in no particular order:

a) “Mesoscopic” corresponding to an scale of observation of severalseconds to tens of seconds (e.g., 10-300 s) to capture at least in part,a change in the shape of heart rate plot representative of a statechange.

b) “Microscopic” corresponding to the scale of observation of at leastpart of a heart beat such as that represented by an EKG's P-QRS-Tcomplex.

c) “Macroscopic” corresponding to an scale of observation longer than300 s to encompass more than the information contained in the mesoscopicscale or window as defined in a).

Seizure detection at a macroscopic scale provides information notobtainable with the two other scales (micro- and mesoscopic) allowingfor the identification of certain patterns (defined herein as theoccurrence of more than one triangle or parabola or combinations thereofwithin a macroscopic window).

A shape deformation (e.g., a deformed “M”) may show local and globalextrema that may be used for detection and validation purposes.

In one embodiment, the method comprises matched filtering. Matchedfiltering is a theoretical framework and not the name of a specificfilter. A matched filter is a type of filter matched to the known orassumed characteristics of a target signal and is designed to optimizethe detection of that signal in the presence of noise as it maximizesS/N. A matched filter's impulse response is equal to the time reversed,complex conjugate impulse response of the input.

The output response of a “matched” filter derived from meso-, micro- ormacroscopic patterns, as it is passed through any of these patterns ischaracteristic (it forms a spatio-temporal pattern) and in turn may beused not only to validate detections but to allow detections before theconvolution is completed (“early” detection).

A second filter matched to the first matched filter's output responsemay be run simultaneously with the first matched filter and its outputresponse may be used for early detection and second level validation ofthe detection.

The pattern formed by any of the cardiac activity parameters may used asa matched filter. Other realizations such as the orthogonal andprojected orthogonal matched filter detection (Eldar Y C. Oppenheim A,Egnor D. Signal Processing 2004; 84: 677-693), adaptive matched filterand parametric adaptive matched filter (Dong Y. Parametric adaptivefilter and its modified version DSTO-RR-0313 My 2006 AustralianGovernment, Dept. of Defence); the nearest matched filter fprclassification of spatio-temporal patterns (Hecht-Nielsen R. AppliedOptics 1987; 26:1892-98), an outlier resistant matched filter (GerlachK. IEEE Trans Aerospace Electronic Syst 2002; 38:885-901), a phase-onlymatched filter (Horner J L, Gianino P D. Applied Optics 1984; 23:812-16)may be also used for detection and validation of state changes suchepileptic seizure.

The detection and validation of states based on the morphology or shapeof signals may be performed at various time scales (micro-, meso-, ormacroscopic) through estimation of the autocorrelation function of saidshapes or patterns. Furthermore, estimation of the autocorrelationfunction of a reference state may also be used for detection andvalidation of state changes alone or in combination with theautocorrelation estimates of the state change shapes or patterns.Autocorrelation may be considered as an equivalent method to matchedfiltering.

Other methods such as non-linear detectors (Theiler J, Foy B R, Fraser AM. Beyond the adaptive matched filter. Non-linear detectors for weaksignals in high dimensional clutter. Proc SPIE 6565 (2007) 6565-02:1-12) and maximum likelihood estimation (Formey G D, Maximum-likelihoodestimation of digital sequences in the presence of intersymbolinterference. IEEE Trans Information Theory 1972; 18:363-76) may be alsoapplied in this invention.

Matching a heart rate shape to a state change template can be performedby any appropriate mathematical technique. For example, pattern matchingby use of a matched filter is generally known to one skilled in the art.In one embodiment, the state change template comprises at least onematched filter. In one embodiment, a “match” refers to a match scorefound by a matched filter analysis of greater than about 0.75, such asgreater than about 0.80, greater than about 0.85, greater than about0.90, greater than about 0.95, greater than about 0.98, or greater thanabout 0.99. A “failure to match” refers to a match score found by amatched filter analysis of less than about 0.75, such as less than about0.80, less than about 0.85, less than about 0.90, less than about 0.95,less than about 0.98, or less than about 0.99. However, these values maybe changed as needed.

In one embodiment, the state change template comprises at least a statechange matched filter and a reference parameter matched filter. A“match” can be defined as a match to the state change matched filter notaccompanied by a match to the reference parameter filter.

Regardless of the type of filter, in one embodiment, the heart ratederivative shape has a matched filter score to said state changetemplate greater than a value threshold for at least a durationthreshold. For example, any of the values set forth above may be used asthe value threshold and the duration threshold may be selected as anyappropriate number of seconds or heart beats, such as 1 to 10 sec, or 1to 10 beats, such as 3 beats.

In one embodiment, the state change template exists in a first timescaleand said heart rate derivative shape is present in said first timescale.For example, the heart rate derivative shape is present over a firsttimescale not typically found in a reference heart rate derivative shapeobserved during rising from lying to sitting, rising from sitting tostanding, minor physical exertion, exercise, or emotionally-intenseexperiences. This allows distinction between heart rate derivativeshapes associated with a state change of interest, e.g., an epilepticseizure, and heart rate derivative shapes associated with normal dailyactivities.

In one embodiment, the state change template comprises at least onepositive phase and at least one negative phase. In a further embodiment,the at least one positive phase is a period of elevated heart rate. Inan even further embodiment, the period of elevated heart rate is aperiod of tachycardia. In people fifteen years of age and older,tachycardia is defined as a heart rate greater than 100 bpm. In anotherfurther embodiment, the at least one negative phase is a period ofdecreased heart rate. In an even further embodiment, the period ofdecreased heart rate is a period of bradycardia. Bradycardia is definedin adults as a heart rate less than 60 bpm.

In one embodiment, the state change template comprises at least twoextrema of heart rate. In a further embodiment, the state changetemplate can also comprise at least two phases.

The state change template may comprise one or more shapes readilydiscernible to the human eye. For example, the state change template maycomprise a triangle, such as that shown in FIG. 6. Although in manycases, state change templates that appear more complex than a trianglemay be useful, they can generally be understood as involving one or moretriangles or parabolas and/or deformations thereof.

FIG. 5 illustrates the metamorphosis or transformation of circumictalheart rate shapes or patterns at a mesoscopic scale. The simplest shapeis that of a parabola (left upper panel). In certain seizures ashort-lived withdrawal or reduction of sympathetic influences or anincrease in parasympathetic ones early in the course of a seizure causesa notch or indentation in the parabola (right upper panel). In otherseizures (in the same subject or in a different subject), a later, morepronounced and prolonged withdrawal or reduction of sympatheticinfluences or an increase in parasympathetic ones (compared to that seenin the right upper panel) leads to a prominent indentation or notch(right lower panel), resembling the letter “M”. A later, briefer, andless pronounced withdrawal or reduction of sympathetic influences or anincrease in parasympathetic ones (compared to that seen in the rightlower panel) causes an indentation in the parabola.

The relative balance of sympathetic and parasympathetic influences canbe assayed at multiple timescales. As can be seen with reference to atleast some of the figures discussed below, the relative balance ofsympathetic and parasympathetic influences can oscillate on multipletimescales.

While a parabola is shown in FIG. 5 as an example, this may be replacedby a triangle or by any other topologically equivalent shape.

We have discovered a number of specific patterns or shapes occurring inat least some circumictal periods of at least some patients, whichpatterns or shapes may be used as the basis for a state change templateas discussed herein.

Generally, the specific patterns or shapes can be considered asbelonging to one of three categories:

Simple patterns, including the parabola shown in FIG. 5 or the triangleshown in FIG. 6, among others;

Complex patterns, including the notched triangle pattern of FIG. 7, the“M” pattern of FIG. 8, and the “W” pattern of FIG. 9, among others;

Polymorphic patterns, containing two or more simple and/or complexpatterns, including fused simple and/or complex patterns, periodic orquasiperiodic oscillations, periodic or quasiperiodic oscillationsoverlaid on a longer term simple and/or complex patterns, and multiplesimple and/or complex patterns, such as those shown in FIGS. 10-17,among others.

Exemplary patterns or shapes are shown in FIGS. 6-17. In each of thesefigures, a relevant portion of a graph of a patient's heart rate inbeats per minute (BPM) vs. time in hours from the onset of ECoGmonitoring of his or her seizure activity is shown. Vertical lines markthe electrographic onset and electrographic termination of a seizure.

The reader will have noticed that some patterns notable in FIGS. 6-17 asbeing closely correlated in time with a seizure also occur at times whenno seizure was detected by ECoG. It should be pointed out that sincemonitoring of brain activity with intracranial electrodes is limited tocertain regions, seizures may occur and go undetected if they originatein regions not monitored by the available electrodes. This may explainthe presence of multiple heart rate patterns in the circumictal periodwhen only one seizure was recorded. In other words, the cardiac data mayindicate the occurrence of seizures that intracranial electrodes failedto detect. The use of cardiac information, such as the uses describedand claimed herein, may supplement the inherent limitations ofbrain-based seizure detection.

FIG. 6 shows what may be termed a simple pattern, viz., a triangle, inaccordance with an illustrative embodiment of the present invention.Herein, when discussing shapes, the words “triangle” and “parabola” canbe used interchangeably. Generally, “triangle” will be used forconvenience only.

FIG. 7A-C shows three graphs of what may be termed a notched triangle.

In various examples, the state change template may comprise one or moreshapes that can be considered as comprising a plurality of triangles.For example, the state change template may comprise one or more shapesresembling letters of the Latin alphabet.

FIG. 8A-C shows three graphs of what may be termed an “M” pattern,formed by two contiguous triangles or parabolae. The “M” pattern may bemonophasic (the heart rate does not drop below the reference value orbaseline) or multiphasic (after raising above the reference value, theheart rate drops below it). An “M” can be considered as distinct from a“notched triangle” in that the indentation of the M generally returnssubstantially to a baseline value and generally divides the M intosubstantially symmetrical halves.

The “M” patterns shown in FIGS. 8A-8C have total durations of about60-90 sec, beginning anywhere from about 15 sec before electrographiconset to about 90 sec after electrographic onset. However, other totaldurations and beginning times relative to electrographic onset may occurin other “M” patterns.

FIG. 9 shows a graph of what may be termed a “W” pattern, discerniblefrom about 15 see after electrographic onset to about 20 sec afterelectrographic termination. Though not to be bound by theory, the “W”pattern may reflect differences (compared to the “M” pattern) in thetiming of changes in autonomic influences during seizures.

The triangle, notched triangle, “M,” and “W” patterns of FIGS. 6-9 canbe considered to occur on a mesoscopic timescale. However, the samepatterns may be discerned at shorter or longer timescales.

FIGS. 10-17 show patterns that can be considered to occur at longmesoscopic and/or macroscopic timescales. As can be seen and will bediscussed below, the patterns of FIGS. 10-17 can generally be consideredas polymorphic patterns comprising two or more of the basic shapes,simple patterns, or complex patterns discussed above.

FIG. 10 shows a fused “M” and “W” pattern. The “W” can be considered asstarting at about 30 sec before electrographic onset and ending at about60-75 sec after electrographic onset in the region of highest heart rateduring the seizure event. The “M” can be considered as starting a fewseconds before electrographic onset and ending about at electrographictermination. One may also discern a “W” occurring at a microscopic orshort mesoscopic timescale at the notch of the “M.”

Alternatively or in addition, a person of ordinary skill in the art,having the benefit of the present disclosure, may discern an “M”beginning at about 45-60 sec before electrographic onset and ending atabout the middle of the seizure, with a “W” beginning about 30 sec afterelectrographic onset and ending about 15-30 sec after electrographictermination.

FIG. 11A-B shows two graphs of patterns of periodic or quasiperiodicoscillations. (For convenience, we will use the term “periodic,”although it must be borne in mind that the frequency and the amplitudeof the oscillations associated with a single seizure in one patient mayvary over the course of about 10 min, as shown in FIGS. 11A-B. In otherwords, the term “periodic” is not limited herein to refer to series ofoscillations with fixed frequency and amplitude).

The pattern of periodic oscillations may be deformed by a seizure event(e.g., FIG. 11B). In instances where this is not the case, a dysfunctionof the patient's autonomic nervous system may be indicated. For example,FIG. 11A shows a rapid oscillation of the patient's heart rate by asmuch as 40 BPM in a short time.

Detecting a pattern in a preictal period in a time series of heart ratedata may be considered, at least in some patients, as a “prediction” ofa seizure and/or an indication of a period of greater risk of a seizure.Alternatively or in addition, it may be used to aid detection ofseizures originating in brain regions not surveyed by intracranialelectrodes.

Multiple triangles with a certain degree of periodicity and eithermonophasic or biphasic nature can form what may be viewed as a“sawtooth” pattern in the circumictal period. FIG. 12 shows a graph ofanother pattern of periodic oscillations. The periodic oscillations fromabout 15-30 sec after the seizure to about 3 min after the seizure canbe considered a sawtooth pattern.

FIG. 13A-D shows four graphs of patterns of periodic oscillationsoverlaid on a longer-timescale triangle pattern. For example, thepattern in FIG. 13A shows an asymmetric triangle with a trailing slopelasting about 5 min, on which is overlaid a pattern of periodicoscillations having an average wavelength of about 20 sec is discerniblefrom about 90 sec after the seizure until the end of the window shown.

FIG. 14 shows, in addition to a pattern of periodic oscillationsoverlaid in the post-ictal period on a longer-timescale trianglepattern, a comb pattern in the preictal period. For a duration of about2.5 min starting about 3.5 min before electrographic onset, a pattern ofperiodic oscillations is shown with pronounced negative amplitudes(relative to the average heart rate over the first 30-45 sec of thewindow) and an average wavelength of about 15 sec. Again, detecting apattern in a preictal period in a time series of heart rate data may beconsidered, at least in some patients, as a “prediction” of a seizureand/or an indication of a period of greater risk of a seizure.Alternatively or additionally, the presence of one pattern of longduration or more than one pattern of any duration in the circumictalperiod are indicative of cardiac or autonomic instability. Thisinformation may be used to warn the patient or his caregiver(s) of anincreased risk of a serious outcome and/or institute therapeuticmeasures.

FIG. 15 shows another comb pattern, this one with pronounced positiveamplitudes, overlaid on a longer-timescale parabola.

FIG. 16 shows a triphasic pattern relative to the preictal baseline, inwhich a first positive phase forms a notched triangle from just beforeelectrographic onset until late in the seizure; a second, negative phasefollows until about 30-45 sec after the seizure; and a third, positivephase ensues with a duration of about 4 min until the end of the window.

FIG. 17A-B shows two graphs from which multiple “M” and/or “W” patternsare discernible in all three of the preictal, ictal, and postictal timeperiods. These multiple “M” and/or “W” patterns can be considered aspart of a macroscopic pattern comprising a plurality of complex shapes.

In addition, very rapid oscillations in heart rate may also occur, andalong with lower frequency oscillations, may provide useful insight intothe behavior of heart rate variability circum-ictally and of itsusefulness for seziure detection, given its differences from thoseobserved outside the circum-ictal period. That is, oscillations at twofrequencies (e.g., slow and fast) or more than two frequencies (e.g.,very fast, slow, and very slow) may overlap to form a pattern that iscommonly associated with a circumictal period.

Any one or more of the patterns shown in FIGS. 6-17, among others, canbe taken as the basis for a state change template. Also, HRV values canbe derived from the time series of heart rates depicted in FIGS. 6-17,and one or more distinctive patterns discernible from the HRV values canbe used as the basis for a state change template. Such distinctivepatterns would generally be expected to be distinct from HRV changesresulting from exercise or normal exertion.

Regardless of how HRV values are determined, in one embodiment, thepattern or shape of heart rate variability (as distinct from heart rate)measured at any or all of the timescales (micro-, meso-, or macroscopic)may be used as a template for detection and quantification of statechanges using matched filtering or its autocorrelation function.

In a particular embodiment, the state change template comprises onephase relative to the reference heart rate parameter, three extrema,four directions of heart rate change, and two periods of increased heartrate relative to the reference heart rate parameter. This state changetemplate may be considered to be the “M” pattern shown in FIG. 8.

Multiple state change templates, including but not limited to multipletemplates at different timescales, may be used for various purposes. Forexample, a first template found to have a particularly high sensitivity,specificity, or both can be used as a primary detection technique, withother templates used to validate detections made by the first template.For another example, a template found to have high sensitivity but lowspecificity (i.e., giving detections with a relatively high falsepositive rate) can be paired with another template found to have highspecificity to be used in detections with higher sensitivity andspecificity than either alone. For still another example, a firsttemplate can be used to identify a state change e.g., from anon-circumictal state to a preictal state, and this identification canbe used to trigger use of a second template to identify a second statechange, e.g., from a preictal state to an ictal state. For a particularexample, a comb pattern can be used to identify a state change from anon-circumictal state to a preictal state, and an “M” pattern can beused to identify a state change from a preictal state to an ictal state.

In one embodiment, a plurality of matched filters (and/or the output ofone or more of the matched filters as another matched filter or filters)can be used. For example, two or three matched filters, each on aseparate one of the macroscopic, mesoscopic, and microscopic timescalescan be run simultaneously on the time series of heart rate derivativedata. After adequate analysis, comparisons of the results of matchedfiltering at the three times scales can be made to find the matchedfilter/timescale combination(s) giving highest sensitivity, highestspecificity, fastest detection, or two or more thereof. Depending on theintended use, the most useful matched filter/timescale can then be usedand run continuously and its output (detection) used to run the othermatched filtersitimescales for detection of changes (at longer orshorter time scales) and validation of detected changes.

Alternatively or in addition to the state change detections discussedherein, circumictal changes at various times scales may be used forassessment of disease state, both among circumictal changes monitoredover long time periods (such as months or years) and between circumictaland non-circumictal states. In one embodiment, such disease stateassessment may include assessment of the patient's risk ofepilepsy-related sudden death (SUDEP).

Regardless of the desired use of circumictal data, circumictal changesmay be quantified in one or more dimensions. In one embodiment, theoutput value of a detection, a disease state assessment, or the like canbe monitored as a function of time (days, month years), bothinter-circumictally and circumictally vs. non-circumictally, with theresults analyzed for the presence of changes and trends. In anotherembodiment, circumictal changes can be classified as a function ofpattern type (e.g., simple, complex, or polymorphic) and their temporalevolution tracked. In another embodiment, the temporal density of thecircumictal period can be defined as percent time spent in a pattern(s).

Quantification of the match between the heart rate derivative shape andthe state change template can also provide information about theduration of a seizure. In one embodiment, the method further comprisesindicating the termination of the state change based upon adetermination that the heart rate derivative shape fails to match thestate change template, after an indication of an occurrence of a statechange.

In one embodiment, the state change template further comprises at leastone second characteristic selected from a magnitude of heart rate changerelative to the reference heart rate parameter, a slope of heart ratechange, a duration of one or more phases, a duration from a heart rateexcursion from the reference heart rate parameter to a peak or a troughheart rate, a total duration of all the phases, or a duration of aconstant slope of heart rate change; and indicating an occurrence of astate change is based upon a determination that the heart rate shapematches a state change template in both the at least one characteristicand in the at least one second characteristic.

The slope can be measured on any time scale, though for cardiac data, itmay be smoother if taken over multiple beats, such as five or fifteenbeats, or over a length of time, such as five to fifteen seconds. Theterm “constant slope” is used herein to refer to a fit, such as aleast-squares fit or other fit, of the data series in question that hasa sufficiently high fit to a straight line as to commend itself to theperson of ordinary skill in the art as being a constant. For example, aregion of a data series having a linear least-squares fit with an R²value of at least 0.9 can be considered to have a constant slope.

As stated above, a state change can be indicated by quantifying thematch of the heart rate shape to the state change template. This statechange indication can be considered as the sole indication of a statechange, it can be validated by other techniques of state changeidentification, or it can be used to verify state changes indicated byother techniques. Such other techniques include those describedelsewhere herein, as well as others known to the person of ordinaryskill in the art or others the subject of one or more patentapplications, such as U.S. patent application Ser. Nos. 12/770,562,filed Apr. 29, 2010; 12/771,727, filed Apr. 30, 2010; and 12/771,783,filed Apr. 30, 2010.

In one embodiment, the determination comprises using a first matchedfilter to yield a first output, building a second matched filter fromthe first output, and using the second matched filter to detect thestate change. In other words, because the passage of a first matchedfilter over a data window will produce a stereotypical output when itbegins passing over a shape which it matches, the stereotypical outputitself can be used to detect a state change prior to or as a validationof, a detection by the first matched filter.

Thus, in one embodiment, the method further comprises identifying anoccurrence of a state change; and wherein said determining said heartrate derivative shape and said indicating are performed in response tosaid identifying, to validate said identifying.

In another embodiment, the method further comprises identifying anoccurrence of a state change in response to said indicating, to validatesaid indicating. In a further embodiment, the method further comprisesobtaining data relating to at least a portion of a heart beat complexfrom said patient; comparing said at least said portion of said heartbeat complex with a corresponding portion of a reference heart beatcomplex template of said patient, wherein the reference heart beatcomplex template is not indicative of a state change; and validatingsaid indicating an occurrence of a state change, wherein said validatingis based upon a determination that said heart beat complex fails tomatch said reference heart beat complex template.

In one embodiment, the reference heart beat complex template is selectedfrom a normal template (e.g., a reference heart beat complex templatenot indicative of a state change from a patient with healthy heartactivity) or an abnormal template (e.g., a reference heart beat complextemplate not indicative of a state change from a patient with current orpast unhealthy heart activity).

For example, a heart rate derivative shape present over a firsttimescale not typically found in a reference heart rate derivative shapeobserved during rising from lying to sitting, rising from sitting tostanding, minor physical exertion, exercise, or emotionally-intenseexperiences can be used to indirectly validate an identification of aseizure made from a rise in heart rate, or vice versa.

Alternatively or in addition, in another embodiment, the methodcomprises determining a second reference heart rate parameter,determining a second heart rate derivative shape from said time seriesof cardiac data, wherein said second heart rate derivative shapecomprises at least one second characteristic selected from a number ofphases relative to said reference heart rate parameter, a number ofpositive phases relative to said reference heart rate parameter, anumber of negative phases relative to said reference heart rateparameter, a number of extrema of said second heart rate derivative, ora number of directions of change of said second heart rate derivative;and validating said indicating an occurrence of a state change, whereinsaid validating is based upon a determination that said second heartrate derivative shape matches a second state change template in said atleast one second characteristic.

The present invention also provides a method for indicating anoccurrence of a state change, comprising obtaining data relating to atleast a portion of a heart beat complex from a patient; comparing the atleast the portion of the heart beat complex with a corresponding portionof a reference heart beat complex template of the patient; andindicating an occurrence of a state change based upon a determinationthat the heart beat complex fails to match the reference heart beatcomplex template.

A heart beat complex is used herein to refer to a PQRST complex, as isknown from the electrocardiography (EKG) art, from a single heart beat,including both the relative and absolute magnitudes of the P-, Q-, R-,S-, and T-waves, and all of the intervals P-Q, P-R, P-S, P-T, Q-R, Q-S,Q-T, R-S. R-T, and S-T. A portion of the heart beat complex is then anyone or more of the relative and/or absolute magnitudes of the waves,their shapes, and/or one or more of the intervals between waves. Arelative magnitude may be defined according to any one or more of thewaves of the complex, e.g., an R-wave amplitude can be defined as rtimes the P-wave amplitude. FIG. 18 shows exemplary heart beat complexeswith P- and R-waves identified by name. The horizontal lines are drawnfor convenience, to point out plausible deviations between the variouswaves of different beat complex.

Although the term “a heart beat complex” is used above, a plurality,such as, but not necessarily, a sequential plurality, of heart beatcomplexes can be used, with the comparing being done for one or more ofthe plurality of heart beat complexes. The plurality may be a fixed setof beats or a moving window over a predetermined time or number ofbeats.

In one embodiment, the portion of the heart beat complex comprises atleast one of an amplitude of a P wave, a polarity of a P wave, at leastone of an amplitude of an R wave, a polarity of a Q wave, a polarity ofan R wave, an amplitude of an S wave, a polarity of an S wave a polarityof an S wave, an amplitude of a T wave, a polarity of a T wave, an areaunder the curve of a P wave, an area under the curve of a Q wave, anarea under the curve of an R wave, an area under the curve of an S wave,an area under the curve of a T wave, a width of a P wave, a with of a Qwave, a width of an n R wave, a width of an S wave, a width of a T wave,a morphology of a P wave, a morphology of a Q wave, a morphology of an Rwave, a morphology of a T wave, a magnitude of a change in the distancefrom a P wave to a Q wave, a magnitude of a change in the distance froma P wave to an R wave, a magnitude of a change in the distance from a Qwave to an R wave, a magnitude of a change in the distance from an Rwave to an S wave, a magnitude of a change in the distance from an Rwave to a T wave, a magnitude of a change in the distance from an S waveto a T wave, a magnitude of an S-T segment elevation, a magnitude of anS-T segment depression, a magnitude of a Q-T segment elevation, amagnitude of a Q-T segment depression, a P-R interval, an R-S interval,an S-T interval, an R-T interval, and a Q-T interval.

The reference heart beat complex template can be derived from anynon-state change heart beats. Such beats may be one, some, or all thesame beats used to define the reference heart rate parameter and/orreference HRV described above, but need not be any of the same beats. Inone embodiment, the reference heart beat complex template comprises atleast one matched filter. In a further embodiment, the heart beatcomplex fails to match the reference heart beat complex template if amatched filter score for the heart beat complex to the at least onematched filter is less than a heart beat complex value threshold.

Although one reference heart beat complex template is referred to above,a plurality of reference heart beat complexes may be used. For example,a plurality of reference heart beat complexes can be used on the sameheart beats, or one or more of the plurality can be used at differenttimes of day, under different states of exertion or arousal, in view ofchanges in heart health histories or differences in heart health betweenpatients, among other possibilities. In one embodiment, a secondreference heart beat complex template comprises at least one of T wavedepression, P-Q segment elongation, another abnormality, or two or morethereof, relative to the canonical “normal” heart beat complex.

Alternatively, one or more heart beat complex templates derived fromheart beat complexes observed during one or more periods of state changemay be used, with a state change declared if the heart beat complex(es)match(es) the state change heart beat complex template(s).

FIG. 19A shows an exemplary heart beat complex derived from datacollected over an entire period of EKG monitoring of a patient, whichmay be used as a reference heart beat complex template. FIG. 19B showsan exemplary heart beat complex derived from EKG data collected from thesame patient during circumictal periods only, which may be used as astate change heart beat complex template.

In the event a plurality of reference heart beat complex templates areused, one or more of the templates may be modified over time, based onobserved changes in the patient's heart beat complexes, such as duringnon-state-change periods.

The at least portion of the heart beat complex and the correspondingportion of the reference heart beat complex template can be comparedusing any of the pattern matching techniques described herein. Becausethe reference heart beat complex template is taken from non-seizureheart beats, a failure to match between the at least portion of theheart beat complex and the corresponding portion of the reference heartbeat complex template is an indirect indication of a seizure.

Quantification of the match between a portion of a heart beat complexand the corresponding portion of the reference heart beat complextemplate can also provide information about the duration of a seizure.In one embodiment, the method further comprises obtaining a time seriesof data relating to a plurality of heart beat complexes from thepatient; comparing at least a portion of each of a sequential pluralityof heart beat complexes with a corresponding portion of the firstreference heart beat complex template; and indicating the termination ofthe state change based upon a determination that at least one heart beatcomplex of the sequential plurality matches the reference heart beatcomplex template, after an indication of an occurrence of a statechange. Matched filters can be used in this determination, as describedelsewhere herein.

In one embodiment, the determination further comprises analyzing one ormore of a pulse shape, an R wave amplitude, an apex cardiogram, or apressure wave, to validate or classify the state change.

In one embodiment, a heart beat complex fails to match a reference heartbeat complex template if a matched filter output for said heart beatcomplex is less than a first matched filter threshold, or differs from asecond matched filter threshold by at least a predetermined magnitude.

Also similarly to the heart rate derivatives described above, a statechange can be indicated by quantifying the match of the portion of theheart beat complex to the reference heart beat complex template. Thisstate change indication can be considered as the sole indication of astate change, it can be validated by other techniques of state changeidentification, or it can be used to verify state changes indicated byother techniques. Such other techniques include those describedelsewhere herein, as well as others known to the person of ordinaryskill in the art or others the subject of one or more patentapplications, such as U.S. patent application Ser. Nos. 12/770,562,filed Apr. 29, 2010; 12/771,727, filed Apr. 30, 2010; and 12/771,783,filed Apr. 30, 2010.

Thus, in one embodiment, the method further comprises identifying anoccurrence of a state change; wherein the obtaining, the comparing, andthe indicating are performed in response to the identifying, to validatethe identifying.

Particularly, the prior indicating can be performed using heart rate orHRV data, and in one embodiment, one or more heart beats taken from thereference heart rate parameter of the heart rate or HRV data can be usedto define the reference heart beat complex template and one or moreheart beats taken from the excursion of the heart rate or HRV data fromits reference heart rate parameter can be used to as the heart beatcomplex from which a portion is matched with a corresponding portionfrom the reference heart beat complex template. By “zooming” from theheart rate or HRV shape into one or more individual heart beats givingrise to the heart rate or HRV shape, a state change indication from HRVdata can be validated. For example, if the heart rate or HRV shape givesan indication of a state change, but one or more heart beat complexesfrom the putative state change match the reference heart beat complextemplate, the excursion of heart rate or HRV from the reference heartrate parameter may be considered to result from exercise or anothernon-seizure-event source.

In another embodiment, the method further comprises identifying anoccurrence of a state change in response to said indicating, to validatesaid indicating. For example, identifying an occurrence of a statechange to validate an indication can be performed by using a priordetection algorithm, using a second characteristic of the state changetemplate, or matching at least a portion of a heart beat complex with acorresponding portion from a reference heart beat complex template,among other techniques.

The present invention also provides a method for identifying a statechange template from cardiac data, comprising obtaining a time series ofcardiac data from a patient during a first time window; determining atime of occurrence of at least one state change suffered by the patientduring the first time window; and either (i) determining at least onestate change template in the time series of cardiac data within thefirst time window and timewise correlated with the at least one statechange, wherein the at least one state change template comprises atleast one characteristic selected from a number of phases relative to areference heart rate parameter, a number of extrema, a number ofdirections of change, a number of positive phases relative to saidreference heart rate parameter, or a number of negative phases relativeto said reference heart rate parameter, or (ii) determining at least onereference heart beat complex template in the time series of cardiac datawithin the first time window and not timewise correlated with the atleast one state change.

In a particular embodiment, the at least one characteristic comprises atleast one of the amplitude of at least one phase, the duration of atleast one phase, the valence (positive or negative) of at least onephase, at least one slope of at least one phase, the arc length of atleast one phase, the number of extrema in at least one phase, and thesharpness of the extrema of at least one phase.

The cardiac data can comprise one or more of heart rate data, HRV data,or heart beat complex data, such as data from at least a portion of eachof a plurality of heart beat complexes, among others. The cardiac datacan be derived from signals collected from or related to EKG, heartsounds (such as can be collected by a microphone mounted on the skin ofthe chest), blood pressure, apex cardiography, echocardiograpohy,thermography, or blood flow velocities estimated by Doppler imaging,among other techniques known to the person of ordinary skill in the art.

The time of occurrence of the at least one state change can bedetermined by any appropriate technique, such as EEG, cardiac-basedseizure detection (such as that disclosed in U.S. patent applicationSer. Nos. 12/770,562, filed Apr. 29, 2010; 12/771,727, filed Apr. 30,2010; and 12/771,783, filed Apr. 30, 2010), testing of the patient'sresponsiveness (such as that disclosed in U.S. patent application Ser.No. 12/756,065, filed Apr. 7, 2010, the disclosure of which is herebyincorporated herein by reference), among other techniques known to theperson of ordinary skill in the art or otherwise available.

The finding of a timewise correlation of at least one state changetemplate with a state change, or the finding of a non-timewisecorrelation of at least one reference heart beat complex template with astate change, can be performed by any appropriate technique. “Timewisecorrelation” refers to any substantially repeated duration between aputative template and a state change, and includes putative templatestaking place before a state change, during a state change, or after astate change.

The state change template can be further defined according to at leastone second characteristic selected from a magnitude of cardiac datavalue change relative to the reference heart rate parameter cardiac dataseries, a slope of cardiac data value change, a duration of one or morephases, a duration from a cardiac data excursion from the referenceheart rate parameter cardiac data series to a peak or a trough cardiacdata series, a total duration of a cardiac data excursion from thereference heart rate parameter cardiac data series, or a duration of aconstant slope of cardiac data series change.

In another embodiment, the present invention relates to a method fordetermining at least one property of a pattern indicative of anoccurrence of a state change. In one embodiment, this method comprisesobtaining a time series of cardiac data from a patient; determining ifat least one heart rate derivative shape forms at least one pattern; anddetermining at least one property of the pattern.

For example, in one embodiment, the at least one property of the patterncomprises a shape of the pattern, a time of occurrence of the pattern, atime elapsed between occurrences of the pattern, and an association ofthe pattern with a state change of a body organ.

Any state change of any body organ may be considered. In one embodiment,the at least one property of the pattern is an association of thepattern with a state change of the brain. In a further embodiment, thestate change of the brain is a epileptic seizure.

The state change template or reference heart beat complex templateproduced by the present method can be used in a method as describedabove.

However the state change is identified, and regardless of the statechange template, the timescale, and the subperiod of the circumictalperiod in which state changes are detected, in some embodiments, anindication of a state change can be used as the basis for taking aresponsive action selected from warning, logging the time of a statechange, computing and storing one or more state change severity indices,treating the state change, or two or more thereof. In one embodiment,quantification of one or more state change severity indices can beperformed through comparisons of matched filtering outputs, althoughscaling and/or other appropriate transformation may be required when theshapes are similar but their sizes are not.

A state change warning may be given as, for example, a warning tone orlight, vibration, pressure, or scent implemented by a medical device ora device adapted to receive indications of the state change; as anautomated email, text message, telephone call, or video message sentfrom a medical device or a unit in communication with a medical deviceto the patient's cellular telephone, PDA, computer, television, 911 oranother emergency contact number for paramedic/EMT services, etc. Such awarning may allow the patient or his or her caregivers to take measuresprotective of patient's well-being and those of others, e.g., pullingout of traffic and turning off a car, when the patient is driving;stopping the use of machinery, contacting another adult if the patientis providing childcare, removing the patient from a swimming pool orbathtub, lying down or sitting if the patient is standing, etc.

The time may be logged by receiving an indication of the current timeand associating the indication of the current time with an indication ofthe state change.

State change severity indices may be calculated and stored byappropriate techniques and apparatus.

In an exemplary embodiment of the present invention, any method ofindicating a seizure can further comprise taking a responsive actionbased upon the identifying the state change. The responsive action mayinclude providing a warning and/or notifying the patient or a caregiver,logging the time of a state change, computing and storing one or morestate change severity indices, or treating the state change.

In one embodiment of the present invention, treating the state changecomprises providing a neurostimulation therapy. The neurostimulationtherapy may involve applying an electrical, mechanical, magnetic,electro-magnetic, photonic, acoustic, cognitive, sensori-perceptualand/or chemical signal to a neural structure of the body. The neuralstructure may be a brain, a spinal cord, a peripheral nerve, a cranialnerve, or another neural structure. In a particular embodiment, theresponsive action comprises treating the state change by providing acranial nerve stimulation therapy. Cranial nerve stimulation has beenproposed to treat a number of medical conditions pertaining to ormediated by one or more structures of the nervous system, includingepilepsy, movement disorders, depression, anxiety disorders and otherneuropsychiatric disorders, dementia, traumatic brain injury, coma,migraine headache, obesity, eating disorders, sleep disorders, cardiacdisorders (such as congestive heart failure and atrial fibrillation),hypertension, endocrine disorders (such as diabetes and hypoglycemia),and pain (including neuropathic pain and fibromyalgia), among others.See, e.g., U.S. Pats. Nos. 4,867,164; 5,299,569; 5,269,303; 5,571,150;5,215,086; 5,188,104; 5,263,480; 6,587,719; 6,609,025; 5,335,657;6,622,041; 5,916,239; 5,707,400; 5,231,988; and 5,330,515.

In some embodiments, electrical neurostimulation may be provided byimplanting an electrical device underneath the skin of a patient anddelivering an electrical signal to a nerve such as a cranial nerve. Inanother alternative embodiment, the signal may be generated by anexternal pulse generator outside the patient's body, coupled by an RF orwireless link to an implanted electrode. In one embodiment, thetreatment comprises at least one of applying an electrical signal to aneural structure of a patient; delivering a drug to a patient; orcooling a neural structure of a patient. When the treatment comprisesapplying an electrical signal to a portion of a neural structure of apatient, the neural structure may be at least one of a portion of abrain structure of the patient, a portion of a cranial nerve of apatient, a portion of a spinal cord of a patient, a portion of asympathetic nerve structure of the patient, a portion of aparasympathetic nerve structure of the patient, and/or a portion of aperipheral nerve of the patient.

The above methods can be performed alone. In one embodiment, the abovemethods can be performed in combination with a continuous or open-looptherapy for epilepsy. In one embodiment, the above methods are performedto take action in response to an indication of a state change, and atall or most other times, a chronic therapy signal is applied to a targetstructure in the patient's body. In one embodiment, the target structureis a cranial nerve, such as the vagus nerve.

Although not limited to the following, an exemplary system capable ofimplementing embodiments of the present invention is described below.FIG. 1 depicts a stylized implantable medical system (IMD) 100 forimplementing one or more embodiments of the present invention. Anelectrical signal generator 110 is provided, having a main body 112comprising a case or shell with a header 116 for connecting to aninsulated, electrically conductive lead assembly 122. The generator 110is implanted in the patient's chest in a pocket or cavity formed by theimplanting surgeon just below the skin (indicated by a dotted line 145),similar to the implantation procedure for a pacemaker pulse generator.

A nerve electrode assembly 125, preferably comprising a plurality ofelectrodes having at least an electrode pair, is conductively connectedto the distal end of the lead assembly 122, which preferably comprises aplurality of lead wires (one wire for each electrode). Each electrode inthe electrode assembly 125 may operate independently or alternatively,may operate in conjunction with the other electrodes. In one embodiment,the electrode assembly 125 comprises at least a cathode and an anode. Inanother embodiment, the electrode assembly comprises one or moreunipolar electrodes.

Lead assembly 122 is attached at its proximal end to connectors on theheader 116 of generator 110. The electrode assembly 125 may besurgically coupled to the vagus nerve 127 in the patient's neck or atanother location, e.g., near the patient's diaphragm or at theesophagus/stomach junction. Other (or additional) cranial nerves such asthe trigeminal and/or glossopharyngeal nerves may also be used todeliver the electrical signal in particular alternative embodiments. Inone embodiment, the electrode assembly 125 comprises a bipolarstimulating electrode pair 126, 128 (i.e., a cathode and an anode).Suitable electrode assemblies are available from Cyberonics, Inc.,Houston, Tex., USA as the Model 302 electrode assembly. However, personsof skill in the art will appreciate that many electrode designs could beused in the present invention. In one embodiment, the two electrodes arewrapped about the vagus nerve, and the electrode assembly 125 may besecured to the vagus nerve 127 by a spiral anchoring tether 130 such asthat disclosed in U.S. Pat. No. 4,979,511 issued Dec. 25, 1990 to ReeseS. Terry, Jr. Lead assembly 122 may be secured, while retaining theability to flex with movement of the chest and neck, by a sutureconnection to nearby tissue (not shown).

In alternative embodiments, the electrode assembly 125 may comprisetemperature sensing elements, blood pressure sensing elements, and/orheart rate sensor elements. Other sensors for other body parameters mayalso be employed. Both passive and active stimulation may be combined ordelivered by a single IMD according to the present invention. Either orboth modes may be appropriate to treat a specific patient underobservation.

The electrical pulse generator 110 may be programmed with an externaldevice (ED) such as computer 150 using programming software known in theart. A programming wand 155 may be coupled to the computer 150 as partof the ED to facilitate radio frequency (RF) communication between thecomputer 150 and the pulse generator 110. The programming wand 155 andcomputer 150 permit non-invasive communication with the generator 110after the latter is implanted. In systems where the computer 150 usesone or more channels in the Medical Implant Communications Service(MICS) bandwidths, the programming wand 155 may be omitted to permitmore convenient communication directly between the computer 150 and thepulse generator 110.

Turning now to FIG. 2A, a block diagram depiction of a medical device200 is provided, in accordance with one illustrative embodiment of thepresent invention.

In some embodiments, the medical device 200 may be implantable (such asimplantable electrical signal generator 110 from FIG. 1), while in otherembodiments the medical device 200 may be completely external to thebody of the patient.

The medical device 200 (such as generator 110 from FIG. 1) may comprisea controller 210 capable of controlling various aspects of the operationof the medical device 200. The controller 210 is capable of receivinginternal data or external data, and in one embodiment, is capable ofcausing a stimulation unit 220 (FIG. 2B) to generate and deliver anelectrical signal to target tissues of the patient's body for treating amedical condition. For example, the controller 210 may receive manualinstructions from an operator externally, or may cause the electricalsignal to be generated and delivered based on internal calculations andprogramming. In other embodiments, the medical device 200 does notcomprise a stimulation unit 220 (FIG. 2A). In either embodiment, thecontroller 210 is capable of affecting substantially all functions ofthe medical device 200.

The controller 210 may comprise various components, such as a processor215, a memory 217, etc. The processor 215 may comprise one or moremicrocontrollers, microprocessors, etc., capable of performing variousexecutions of software components. The memory 217 may comprise variousmemory portions where a number of types of data (e.g., internal data,external data instructions, software codes, status data, diagnosticdata, etc.) may be stored. The memory 217 may comprise one or more ofrandom access memory (RAM), dynamic random access memory (DRAM),electrically erasable programmable read-only memory (EEPROM), flashmemory, etc.

As stated above, in one embodiment, the medical device 200 may alsocomprise a stimulation unit 220 capable of generating and deliveringelectrical signals to one or more electrodes 126, 128 via leads 201(FIG. 2B). A lead assembly such as lead assembly 122 (FIG. 1) may becoupled to the medical device 200. Therapy may be delivered to the leads201 comprising the lead assembly 122 by the stimulation unit 220 basedupon instructions from the controller 210. The stimulation unit 220 maycomprise various circuitry, such as electrical signal generators,impedance control circuitry to control the impedance “seen” by theleads, and other circuitry that receives instructions relating to thedelivery of the electrical signal to tissue. The stimulation unit 220 iscapable of delivering electrical signals over the leads 201 comprisingthe lead assembly 122. As should be apparent, in certain embodiments,the medical device 200 does not comprise a stimulation unit 220, leadassembly 122, or leads 201.

In other embodiments, a lead 201 is operatively coupled to an electrode,wherein the electrode is adapted to couple to at least one of a portionof a brain structure of the patient, a cranial nerve of a patient, aspinal cord of a patient, a sympathetic nerve structure of the patient,or a peripheral nerve of the patient.

The medical device 200 may also comprise a power supply 230. The powersupply 230 may comprise a battery, voltage regulators, capacitors, etc.,to provide power for the operation of the medical device 200, includingdelivering the therapeutic electrical signal. The power supply 230comprises a power source that in some embodiments may be rechargeable.In other embodiments, a non-rechargeable power source may be used. Thepower supply 230 provides power for the operation of the medical device200, including electronic operations and the electrical signalgeneration and delivery functions. The power supply 230 may comprise alithium/thionyl chloride cell or a lithium/carbon monofluoride (LiCFx)cell if the medical device 200 is implantable, or may compriseconventional watch or 9V batteries for external (i.e., non-implantable)embodiments. Other battery types known in the art of medical devices mayalso be used.

The medical device 200 may also comprise a communication unit 260capable of facilitating communications between the medical device 200and various devices. In particular, the communication unit 260 iscapable of providing transmission and reception of electronic signals toand from a monitoring unit 270, such as a handheld computer or PDA thatcan communicate with the medical device 200 wirelessly or by cable. Thecommunication unit 260 may include hardware, software, firmware, or anycombination thereof.

The medical device 200 may also comprise one or more sensor(s) 212coupled via sensor lead(s) 211 to the medical device 200. The sensor(s)212 are capable of receiving signals related to a physiologicalparameter, such as the patient's heart beat, blood pressure, and/ortemperature, and delivering the signals to the medical device 200. Inone embodiment, the sensor(s) 212 may be the same as implantedelectrode(s) 126, 128 (FIG. 1). In other embodiments, the sensor(s) 212are external structures that may be placed on the patient's skin, suchas over the patient's heart or elsewhere on the patient's torso.

In one embodiment, the medical device 200 may comprise a cardiac datacollection module 265 that is capable of collecting cardiac datacomprising fiducial time markers of each of a plurality of heart beats.The cardiac data collection module 265 may also process or condition thecardiac data. The cardiac data may be provided by the sensor(s) 212. Thecardiac data collection module 265 may be capable of performing anynecessary or suitable amplifying, filtering, and performinganalog-to-digital (A/D) conversions to prepare the signals fordownstream processing. The cardiac data collection module, in oneembodiment, may comprise software module(s) that are capable ofperforming various interface functions, filtering functions, etc., toprocess fiducial time markers of each of a plurality of heart beats. Inanother embodiment the cardiac data collection module 265 may comprisehardware circuitry that is capable of performing these functions. In yetanother embodiment, the cardiac data collection module 265 may comprisehardware, firmware, software and/or any combination thereof. A moredetailed illustration of the cardiac data collection module 265 isprovided in FIG. 3A and accompanying description below.

The cardiac data collection module 265 is capable of collecting cardiacdata comprising fiducial time markers of each of a plurality ofcandidate heart beats and providing the collected cardiac data to aheart beat/interval determination module 275. Based upon the signalsprocessed by the cardiac data collection module 265, the heartbeat/interval determination module 275 may calculate an interbeatinterval from a consecutive pair of the fiducial time markers and storesuch interbeat interval or forward it on for furtherprocessing/analysis. The heart beat/interval determination module 275may comprise software module(s) that are capable of performing variousinterface functions, filtering functions, etc., to calculate interbeatintervals. In another embodiment the heart beat/interval determinationmodule 275 may comprise hardware circuitry that is capable of performingthese functions. In yet another embodiment, the heart beat/intervaldetermination module 275 may comprise hardware, firmware, softwareand/or any combination thereof. Further description of the heartbeat/interval determination module 275 is provided in FIG. 3B andaccompanying description below.

The heart beat/interval determination module 275 is capable ofcalculating an interbeat interval and providing the interbeat intervalto the heart rate/heart rate variability (HRV)/complex module 297. Basedupon one or more interbeat intervals received from the heartbeat/interval determination module 275, and/or signals of sufficientsampling rate to provide information regarding the heart beat complexreceived from the cardiac data collection module 265, the HRderivative/complex module 297 determines at least one or more of anheart rate (such as from an interbeat interval determined from aconsecutive pair of fiducial time markers), a heart rate variability(such as from two consecutive interbeat intervals determined fromfiducial time markers), or at least a portion of a heart beat complex.

The HR derivative/complex module 297 may comprise software module(s)that are capable of performing various interface functions, filteringfunctions, etc., to calculate the various values. In another embodimentthe HR derivative/complex module 297 may comprise hardware circuitrythat is capable of performing these functions. In yet anotherembodiment, the HR derivative/complex module 297 may comprise hardware,firmware, software and/or any combination thereof. Further descriptionof the HR derivative/complex module 297 is provided in FIG. 3E andaccompanying description below.

The HR derivative/complex module 297 is capable of forwarding thecalculated information to template match module 299. Based upon theinformation received by the template match module 299, it performs anyoperations desired to indicate a state change. For example, the templatematch module 299 may indicate a state change based on one or more of aheart rate shape matching an appropriate state change template, an HRVshape matching an appropriate state change template, a portion or moreof a heart beat complex failing to match a reference heart beat complextemplate, or two or more of the foregoing. The template match module 299may comprise software module(s) that are capable of performing variousinterface functions, filtering functions, etc., to indicate a statechange. In another embodiment the template match module 299 may comprisehardware circuitry that is capable of performing these functions. In yetanother embodiment, the template match module 299 may comprise hardware,firmware, software and/or any combination thereof. Further descriptionof the template match module 299 is provided in FIG. 3F and accompanyingdescription below.

In addition to components of the medical device 200 described above, animplantable medical system may comprise a storage unit to store anindication of at least one of state change or an increased risk of astate change. The storage unit may be the memory 217 of the medicaldevice 200, another storage unit of the medical device 200, or anexternal database, such as the local database unit 255 or a remotedatabase unit 250. The medical device 200 may communicate the indicationvia the communications unit 260. Alternatively or in addition to anexternal database, the medical device 200 may be adapted to communicatethe indication to at least one of a patient, a caregiver, or ahealthcare provider.

In various embodiments, one or more of the units or modules describedabove may be located in a monitoring unit 270 or a remote device 292,with communications between that unit or module and a unit or modulelocated in the medical device 200 taking place via communication unit260. For example, in one embodiment, one or more of the cardiac datacollection module 265, the heart beat/interval determination module 275,the HR derivative/complex module 297, or the template match module 299may be external to the medical device 200, e.g., in a monitoring unit270. Locating one or more of the cardiac data collection module 265, theheart beat/interval determination module 275, the HR derivative/complexmodule 297, or the template match module 299 outside the medical device200 may be advantageous if the calculation(s) is/are computationallyintensive, in order to reduce energy expenditure and heat generation inthe medical device 200 or to expedite calculation.

The monitoring unit 270 may be a device that is capable of transmittingand receiving data to and from the medical device 200. In oneembodiment, the monitoring unit 270 is a computer system capable ofexecuting a data-acquisition program. The monitoring unit 270 may becontrolled by a healthcare provider, such as a physician, at a basestation in, for example, a doctor's office. In alternative embodiments,the monitoring unit 270 may be controlled by a patient in a systemproviding less interactive communication with the medical device 200than another monitoring unit 270 controlled by a healthcare provider.Whether controlled by the patient or by a healthcare provider, themonitoring unit 270 may be a computer, preferably a handheld computer orPDA, but may alternatively comprise any other device that is capable ofelectronic communications and programming. e.g., hand-held computersystem, a PC computer system, a laptop computer system, a server, apersonal digital assistant (PDA), an Apple-based computer system, acellular telephone, etc. The monitoring unit 270 may download variousparameters and program software into the medical device 200 forprogramming the operation of the medical device, and may also receiveand upload various status conditions and other data from the medicaldevice 200. Communications between the monitoring unit 270 and thecommunication unit 260 in the medical device 200 may occur via awireless or other type of communication, represented generally by line277 in FIG. 2. This may occur using, e.g., wand 155 (FIG. 1) tocommunicate by RF energy with an implantable signal generator 110.Alternatively, the wand may be omitted in some systems, e.g., systems inwhich the MD 200 is non-implantable, or implantable systems in whichmonitoring unit 270 and MD 200 operate in the MICS bandwidths.

In one embodiment, the monitoring unit 270 may comprise a local databaseunit 255. Optionally or alternatively, the monitoring unit 270 may alsobe coupled to a database unit 250, which may be separate from monitoringunit 270 (e.g., a centralized database wirelessly linked to a handheldmonitoring unit 270). The database unit 250 and/or the local databaseunit 255 are capable of storing various patient data. These data maycomprise patient parameter data acquired from a patient's body, therapyparameter data, state change severity data, and/or therapeutic efficacydata. The database unit 250 and/or the local database unit 255 maycomprise data for a plurality of patients, and may be organized andstored in a variety of manners, such as in date format, severity ofdisease format, etc. The database unit 250 and/or the local databaseunit 255 may be relational databases in one embodiment. A physician mayperform various patient management functions (e.g., programmingparameters for a responsive therapy and/or setting thresholds for one ormore detection parameters) using the monitoring unit 270, which mayinclude obtaining and/or analyzing data from the medical device 200and/or data from the database unit 250 and/or the local database unit255. The database unit 250 and/or the local database unit 255 may storevarious patient data.

One or more of the blocks illustrated in the block diagram of themedical device 200 in FIG. 2A or FIG. 2B, may comprise hardware units,software units, firmware units, or any combination thereof.Additionally, one or more blocks illustrated in FIG. 2A-B may becombined with other blocks, which may represent circuit hardware units,software algorithms, etc. Additionally, any number of the circuitry orsoftware units associated with the various blocks illustrated in FIG.2A-B may be combined into a programmable device, such as a fieldprogrammable gate array, an ASIC device, etc.

Turning now to FIG. 3A, a more detailed stylized depiction of thecardiac data collection module 265 of FIG. 2, in accordance with oneillustrative embodiment of the present invention is depicted. In oneembodiment, the cardiac data collection module 265 comprises a cardiacdata signal receiver 410, an analog-to-digital converter (A/D Converter)420, and a cardiac data forwarding unit 425. The cardiac data signalreceiver 410 is capable of receiving the signals from the sensor(s) 212via receiver circuit 412. The signal that is received by the receivercircuit 412 is processed and filtered to enable the data to be furtheranalyzed and/or processed for determining cardiac data, such as thatdescribed above.

The cardiac data signal receiver 410 may comprise amplifier(s) 414 andfilter(s) 416. The amplifiers 414 are capable of buffering andamplifying the input signals received by the receiver circuit 412. Inmany cases, the heart beat signal may be attenuated and may becharacterized by significantly low amplitude responses and signal noise.The amplifier(s) 414 are capable of buffering (amplification by unity)and amplifying the signals for further processing. In one embodiment,the amplifier 414 may comprise op amp circuit(s), digital amplifier(s),buffer amplifiers, and/or the like.

The cardiac data signal receiver 410 may also comprise one or morefilters 416. The filters 416 may comprise analog filter(s), digitalfilter(s), filters implemented by digital signal processing (DSP) meansor methods, etc. The amplified and buffered signal may be filtered toremove various noise signals residing on the signal. The filter 416, forexample, is capable of filtering out various noise signals caused byexternal magnetic fields, electrical fields, noise resulting fromphysiological activity, etc. Signal noise due to breathing or othersignals produced by the patient's body may be filtered.

The cardiac data signal receiver 410 provides amplified, filteredsignals to the A/D converter 420. The A/D converter 420 performs ananalog-to-digital conversion for further processing. The A/D converter420 may be one type of a plurality of converter types with variousaccuracies, such as an 8-bit converter, a 12-bit converter, a 24-bitconverter, a 32-bit converter, a 64-bit converter, a 128-bit converter,a 256-bit converter, etc. The converted digital signal is then providedto a cardiac data forwarding unit 425. In an alternative embodiment, theA/D conversion may be performed prior to filtering or signal processingof the heart beat signal. The converted digital signal is then providedto a cardiac data forwarding unit 425.

The cardiac data forwarding unit 425 is capable of organizing,correlating, stacking, and otherwise processing the digitized, buffered,and filtered cardiac data and forwarding it to the heart beat/intervaldetermination module 275, and/or directly to the HR derivative/complexmodule 297.

Turning now to FIG. 3B, a more detailed stylized depiction of the heartbeat/interval determination module 275 of FIG. 2, in accordance with oneillustrative embodiment of the present invention, is depicted. The heartbeat/interval determination module 275 may comprise a cardiac datareceiving module 430, for receiving a time stamp sequence of candidateheart beats, a heart beat/interval determination module 440, and a heartbeat/interval time series storage unit 450. The heart beat/intervaldetermination module 275 may determine interbeat intervals for adjacentcandidate heart beats as they appear in the time series of signals viathe cardiac data receiving module 430. For example, cardiac datareceiving module 430 may characterize certain data points in the timeseries of signals as being fiducial time markers corresponding to thestart, the peak, or the end of an R-wave of a patient's cardiac cycle.

Once fiducial time markers are determined from the time series ofsignals, the heart heart beat/interval determination module 440 maydetermine the interval between consecutive beats (“interbeat interval”)and forward this information to heart beat/interval time series storage450, which may store one or both of a time stamp series associated withfiducial markers indicating of an individual heart beat and a time stampseries of adjacent interbeat intervals. In some embodiments, heartbeat/interval determination module 440 may calculate an heart rate,heart rate variability (HRV), or at least a portion of a heart beatcomplex. In other embodiments, heart beat/interval determination module440 may calculate a heart rate, heart rate variability (HRV), or both.

Turning now to FIG. 3C, a more detailed stylized depiction of the HRderivative/complex module 297 of FIG. 2, in accordance with oneillustrative embodiment of the present invention, is depicted. In oneembodiment, the HR derivative/complex module 297 may receive variouscardiac data indicative from the cardiac data collection module 265 orthe heart beat/interval determination module 275. In the embodimentdepicted in FIG. 3C, the HR derivative/complex module 297 comprisesunits that perform various calculations, for example, an heart ratecalculation unit 569 may determine a heart rate from some or allinterbeat intervals and/or pairs of heart beats collected and/oridentified by modules 265 or 275. Certain embodiments of the inventionmay also include a heart rate variability unit 571 which determines anHRV value from some or all interbeat intervals and/or pairs of heartbeats collected and/or identified by modules 265 or 275, and/or a heartbeat complex unit 572 which analyzes one or more portions of a heartbeat complex, e.g. relative R-wave and P-wave amplitudes, P-wave toR-wave temporal separations, or the like. Of course, one or more ofunits 569, 571, and 572 may be omitted, if desired.

The HR derivative/complex module 297 need not perform all steps 569-572.Any steps the HR derivative/complex module 297 performs may be in anyorder, not necessarily that shown.

Although the heart rate calculation unit 569, the heart rate variabilityunit 571, and the heart beat complex unit 572 are shown in FIG. 3C ascomponents of HR derivative/complex module 297, in various otherembodiments, one or more of these units can be included in othermodules.

Turning now to FIG. 3D, a more detailed stylized depiction of thetemplate match module 299 of FIG. 2, in accordance with one illustrativeembodiment of the present invention, is depicted. The template matchmodule 299 may receive various data from the HR derivative/complexmodule 297, including, for example, one or more a heart rate shapecharacteristics, one or more HRV shape characteristics, informationregarding one or more portions of a heart beat complex, etc. Based upondata from the HR derivative/complex module 297, the template matchmodule 299 is capable of indicating a state change, such as describedabove.

In the exemplary depiction shown in FIG. 3D, data received from the HRderivative/complex module 297 is forwarded to a template comparison unit587, which determines whether one or more of the heart rate shape. HRVshape, or portion of the heart beat complex matches a relevant template.The determination of a match can be performed by known mathematicaltechniques, such as matched filtering, or the like. A signal indicativeof the occurrence of a state change is provided by state changeindication unit 589 if the template comparison is indicative of a statechange, such as a seizure.

If a state change is identified by template match module 299, in oneembodiment, a response may be implemented, such as those described byU.S. patent application Ser. Nos. 12/770,562, filed Apr. 29, 2010;12/771,727, filed Apr. 30, 2010; and 12/771,783, filed Apr. 30, 2010.

Turning now to FIG. 4, a stylized flowchart depiction of detecting oneparticular type of state change, namely, a seizure, in accordance withone illustrative embodiment of the present invention, is provided. Themedical device 200 receives a cardiac signal (block 710). In specificembodiments, the cardiac data collection module 265 (FIGS. 2 and 3A) ofthe medical device 200 receives the cardiac signal. After performingbuffering, amplification, filtering, and A/D conversion of the cardiacsignal, the heart beat/interval determination module 275 and/or HRderivative/complex module 297 process the heart beat signal to derive HRderivative shapes or heart beat complex morphology (block 720). From thederived shapes or characteristics, it is decided from one or moretemplate matching operations if a state change is indicated (block 730).This decision may be performed by template match module 299.

Based upon the decision (block 730), if no state change is indicated,the medical device 200 continues to receive the heart beat signal (block750, returning flow to block 710).

However, if a state change is indicated in block 730, the medical device200 or an external unit 270 may provide an indication of the statechange occurrence and/or take a responsive action (block 760), such asproviding a warning to the patient or his or her caregivers, physician,etc. (block 775); logging a time of state change (block 777); computingand optionally logging one or more state change severity indices (block779); and/or providing treatment of the state change (block 781). Moredetails on logging, warning, computing seizure severity, and providingtreatment are provided in U.S. patent application Ser. Nos. 12/770,562,filed Apr. 29, 2010; 12/771,727, filed Apr. 30, 2010; 12/771,783, filedApr. 30, 2010; and 12/756,065, filed Apr. 7, 2010.

The above methods may be performed by a computer readable programstorage device encoded with instructions that, when executed by acomputer, perform the method described herein.

All of the methods and apparatuses disclosed and claimed herein may bemade and executed without undue experimentation in light of the presentdisclosure. While the methods and apparatus of this invention have beendescribed in terms of particular embodiments, it will be apparent tothose skilled in the art that variations may be applied to the methodsand apparatus and in the steps, or in the sequence of steps, of themethod described herein without departing from the concept, spirit, andscope of the invention, as defined by the appended claims. It should beespecially apparent that the principles of the invention may be appliedto selected cranial nerves other than, or in addition to, the vagusnerve to achieve particular results in treating patients havingepilepsy, depression, or other medical conditions.

In various embodiments, the present invention relates to the subjectmatter of the following numbered paragraphs:

34. A method for identifying a state change template from cardiac data,comprising:

obtaining a time series of cardiac data from a patient during a firsttime window;

determining a time of occurrence of at least one state change sufferedby said patient during said first time window; and,

either

(i) determining at least one state change template in the time series ofcardiac data within the first time window and timewise correlated withthe at least one state change, wherein the at least one state changetemplate comprises at least one characteristic selected from a number ofphases relative to a reference heart rate parameter, a number ofextrema, area under the curve of at least one phase, a number ofdirections of change, a number of positive phases relative to saidreference heart rate parameter, or a number of negative phases relativeto said reference heart rate parameter, or

(ii) determining at least one reference heart beat complex template insaid time series of cardiac data within said first time window and nottimewise correlated with said at least one state change.

35. The method of numbered paragraph 34, wherein said cardiac datacomprises heart rate data, heart rate variability data, or heart ratevolatility data.

36. The method of numbered paragraph 34, wherein said cardiac datacomprises at least a portion of each of a plurality of heart beatcomplexes.

37. The method of numbered paragraph 34, wherein said at least onecharacteristic comprises at least one of the amplitude of at least onephase, the duration of at least one phase, the valence (positive ornegative) of at least one phase, the area under the curve of at leastone phase, at least one slope of at least one phase, the arc length ofat least one phase, the number of extrema in at least one phase, and thesharpness of the extrema of at least one phase.

38. A method for obtaining a state change template indicative of anoccurrence of a state change of interest, comprising:

obtaining a first time series of cardiac data from a patient, the firsttime series not associated with said state change of interest;

determining at least one reference heart rate parameter from said firsttime series of cardiac data;

obtaining a second time series of cardiac data from said patient, thesecond time series being associated with said state change of interest;

determining at least one property of said heart rate derivative, saidproperty comprising at least one of a number of phases relative to saidreference heart rate parameter, the perimeter of at least one phase, anumber of extrema of said heart rate derivative, the sharpness of saidextrema, a number of directions of change of said heart rate derivative,an area under the curve of at least one phase, a number of positivephases, or a number of negative phases; and

determining that the at least one property of said heart rate derivativeof the state of interest is different from the same at least oneproperty of the heart rate derivative not associated with the state ofinterest

obtaining a state change template associated with said state change ofinterest and comprising said at least one property, from said heart ratederivative and using it as a matched filter to detect said state change.

39. The method of numbered paragraph 38, wherein the at least oneproperty of said pattern comprises a shape of said pattern, a time ofoccurrence of said pattern, a time elapsed between occurrences of saidpattern, and an association of said pattern with a state change of abody organ.

40. The method of numbered paragraph 39, wherein said at least oneproperty of said pattern is an association of said pattern with a statechange of the brain.

41. The method of numbered paragraph 40, wherein said state change ofthe brain is a epileptic seizure.

42. The method of numbered paragraph 38, wherein said heart ratederivative is heart rate.

43. The method of numbered paragraph 38, wherein said heart ratederivative is heart rate variability or heart rate volatility.

44. A method for indicating an occurrence of a state change, comprising:

providing a first template comprising at least one of a microscopicstate change template, a mesoscopic state change template, and amacroscopic state change template;

obtaining a time series of cardiac data from a patient;

determining a first cardiac data derivative shape from said time seriesof cardiac data; and,

indicating an occurrence of a state change based upon a determinationthat said first cardiac data derivative shape matches said firsttemplate.

45. The method of numbered paragraph 44, further comprising:

providing a second template comprising at least one of said microscopicstate change template, said mesoscopic state change template, and saidmacroscopic state change template, wherein said second template is notbased upon a state change template included in said first template;

determining a second cardiac data derivative shape from said time seriesof cardiac data;

and wherein said indicating is based upon a determination that saidfirst cardiac data derivative shape matches said first template and saidsecond cardiac data derivative shape matches said second template.

46. The method of numbered paragraph 44, wherein said determinationcomprises using a matched filter on a moving window of said firstcardiac data derivative, calculating a time series of outputs of saidmatched filter, and declaring said match if said time series of outputsis substantially equal to a time series of expected output values.

101. A method for indicating an occurrence of a state change,comprising:

obtaining a time series of cardiac data from a patient;

selecting at least one parameter from said cardiac data time series;

determining the magnitude, duration, direction and rate of change ofsaid parameter during a reference state wherein said parameter comprisesat least one of a heart rate, a heart rate variability, a heart ratevolatility, a characteristic of the heart's electrical beat, acharacteristic of the heart's beat sounds, a characteristic of theheart's beat contractility and a characteristic of the heart's beatgenerated pressure

indicating the occurrence of a state change when at least one of saidvalues is greater or lower than at least one reference state parametervalue, e.g., for a certain time period.

102. The method of numbered paragraph 101 wherein the parameters' valuesare treated as phases and extreme endowed with shape, curvature, arclength and inflection points

indicating the occurrence of a state change when at least one of theparameters' values is greater or lower than at least one reference stateparameter values, e.g., for a certain time period.

103. The method of numbered paragraph 101 wherein the cardiac's dataparameter values's temporal scale is macroscopic.

104. The method of numbered paragraph 101 wherein the cardiac's dataparameter values's temporal scale is mesoscopic.

105. The method of numbered paragraph 101 wherein the cardiac's dataparameter values's temporal scale is microscopic.

106. A method for indicating an occurrence of a state change,comprising:

obtaining a time series of cardiac data from a patient during areference state;

selecting at least one parameter from said cardiac data during saidreference state wherein said reference parameter comprises at least oneof a heart rate, a heart rate variability, a heart rate volatility, acharacteristic of the heart's electrical beat, a characteristic of theheart's beat sounds, a characteristic of the heart's beat contractilityand a characteristic of the heart's beat generated pressure

constructing a reference template using said at least one referenceparameter value and using said template as a reference matched filter

indicating an occurrence of a state change based upon a determinationthat the output of said at least one reference matched filter reaches avalue outside the range of values characteristic of the reference state

107. The method of numbered paragraph 106 wherein the reference matchedfilter's scale is macroscopic.

108. The method of numbered paragraph 106 wherein the reference matchedfilter's scale is mesoscopic.

109. The method of numbered paragraph 106 wherein the reference matchedfilter's scale is microscopic.

110. A method for indicating an occurrence of a state change,comprising:

obtaining a time series of cardiac data from a patient during anon-reference state;

selecting at least one parameter from said cardiac data during saidnon-reference state wherein said non-reference parameter comprises atleast one of a heart rate, a heart rate variability, a heart ratevolatility, a characteristic of the heart's electrical beat, acharacteristic of the heart's beat sounds, a characteristic of theheart's beat contractility and a characteristic of the heart's beatgenerated pressure

constructing a non-reference template using said at least onenon-reference parameter value and using said non-reference template as anon-reference matched filter

indicating an occurrence of a state change based upon a determinationthat the output of said at least one non-reference matched filterreaches a value characteristic of the non-reference state values.

111. The method of numbered paragraph 110 wherein the non-referencematched filter's scale is macroscopic.

112. The method of numbered paragraph 110 wherein the non-referencematched filter's scale is mesoscopic.

113. The method of numbered paragraph 110 wherein the non-referencematched filter's scale is microscopic.

114. A method for indicating an occurrence of a state change,comprising:

obtaining a time series of cardiac data from a patient;

selecting at least one reference parameter and at least onenon-reference parameter from said cardiac data wherein said parameterscomprise at least one of a heart rate, a heart rate variability, a heartrate volatility, a characteristic of the heart's electrical beat, acharacteristic of the heart's beat sounds, a characteristic of theheart's beat contractility and a characteristic of the heart's beatgenerated pressure

constructing a reference template using said at least one referenceparameter value and using said reference template as a reference matchedfilter

constructing a non-reference template using said at least onenon-reference parameter value and using said non-reference template as anon-reference matched filter

indicating an occurrence of a state change based upon a determinationthat the output of said at least one reference matched filter reaches avalue outside the values characteristic of the reference state valuesand the output of said at least one non-reference matched filter reachesa value characteristic of the non-reference state values.

115. The method of numbered paragraph 114 wherein the scales of thereference and of the non-reference matched filters are macroscopic.

116. The method of numbered paragraph 114 wherein the scales of thereference and of the non-reference matched filters are mesoscopic.

117. The method of numbered paragraph 114 wherein the scales of thereference and of the non-reference matched filters are microscopic.

118. A method for obtaining a state change template indicative of anoccurrence of a state change of interest, comprising:

obtaining a first time series of cardiac data from a patient, the firsttime series not associated with said state change of interest;

determining at least one parameter from said first time series ofcardiac data wherein said parameters comprise at least one of a heartrate, a heart rate variability, a heart rate volatility, acharacteristic of the heart's electrical beat, a characteristic of theheart's beat sounds, a characteristic of the heart's beat contractilityand a characteristic of the heart's beat generated pressure

obtaining a second time series of cardiac data from said patient, thesecond time series being associated with said state change of interest;

determining at least one parameter from said second time series ofcardiac data wherein said parameters comprise at least one of a heartrate, a heart rate variability, a heart rate volatility, acharacteristic of the heart's electrical beat, a characteristic of theheart's beat sounds, a characteristic of the heart's beat contractilityand a characteristic of the heart's beat generated pressure

determining that the at least one parameter from said second time seriesof cardiac data associate with a state change of interest is differentfrom the same at least one parameter of the first time series of cardiacdata not associated with a state change of interest

obtaining a state change template associated with said state change ofinterest and comprising said at least one property,

using said state change template as a matched filter to detect similarstate changes.

119. The method of numbered paragraph 118 wherein the scale of saidtemplate and matched filter associated with a state change of interestis macroscopic.

120. The method of numbered paragraph 118 wherein the scale of saidtemplate and matched filter associated with a state change of interestis mesoscopic.

121. The method of numbered paragraph 118 wherein the scale of saidtemplate and matched filter associated with a state change of interestis microscopic.

The particular embodiments disclosed above are illustrative only as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown other than as describedin the claims below. It is, therefore, evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

1. A non-transitory computer readable program storage unit encoded withinstructions that, when executed by a computer, perform a method forindicating an occurrence of a brain state change, comprising: obtainingshape data relating to a shape of at least a portion of a heart beatcomplex from a patient; comparing said shape data with a correspondingfirst reference shape template for said at least a portion of a heartbeat complex; and, indicating an occurrence of a brain state changebased upon a determination that said shape data fails to match saidfirst reference shape template.
 2. The non-transitory computer readableprogram storage unit of claim 1, wherein said shape data comprises atleast one parameter selected from an amplitude of a P wave, a polarityof a P wave, an amplitude of an R wave, a polarity of a Q wave, apolarity of an R wave, an amplitude of an S wave, a polarity of an Swave, an amplitude of a T wave, a polarity of a T wave, an area underthe curve of a P wave, an area under the curve of a Q wave, an areaunder the curve of an R wave, an area under the curve of an S wave, anarea under the curve of a T wave, a width of a P wave, a width of a Qwave, a width of an R wave, a width of an S wave, a width of a T wave, amorphology of a P wave, a morphology of a Q wave, a morphology of an Rwave, a morphology of a T wave, a magnitude of a change in the distancefrom a P wave to a Q wave, a magnitude of a change in the distance froma P wave to an R wave, a magnitude of a change in the distance from a Qwave to an R wave, a magnitude of a change in the distance from an Rwave to an S wave, a magnitude of a change in the distance from an Rwave to a T wave, a magnitude of a change in the distance from an S waveto a T wave, a magnitude of an S-T segment elevation, a magnitude of anS-T segment depression, a magnitude of a Q-T segment elevation, amagnitude of a Q-T segment depression, a P-R interval, an R-S interval,an S-T interval, an R-T interval, and a Q-T interval.
 3. Thenon-transitory computer readable program storage unit of claim 1,wherein said first reference shape template comprises at least a firstmatched filter.
 4. The non-transitory computer readable program storageunit of claim 1, wherein said comparing further comprises comparing saidshape data with a corresponding second reference shape template for saidat least a portion of a heart beat complex of said patient, and whereinsaid indicating is based upon a determination that said shape data failsto match at least one of said first reference share template and saidsecond reference shape template.
 5. The non-transitory computer readableprogram storage unit of claim 4, wherein said second reference shapetemplate comprises a brain state change template, and wherein saidindicating said occurrence of said brain state change is based upon bothsaid determination that said shape data fails to match said firstreference shape template and a determination that said shape datamatches said second reference shape template.
 6. The non-transitorycomputer readable program storage unit of claim 5, wherein said firstreference shape template is a non-ictal template and said secondreference shape template is an ictal template.
 7. The non-transitorycomputer readable program storage unit of claim 3, wherein said shapedata fails to match said first reference shape template if a firstmatched filter output for said first reference shape template is lessthan a first matched filter threshold, or differs from said firstmatched filter threshold by at least a predetermined magnitude. 8.-13.(canceled)
 14. The non-transitory computer readable program storage unitof claim 1, wherein said method further comprises: obtaining secondshape data relating to a shape of a plurality of heart beat complexesfrom said patient, said obtaining occurring after said indicating anoccurrence of a brain state change; comparing said second shape datawith said first reference shape template; and, indicating a terminationof said brain state change based upon a determination that said secondshape data matches said first reference shape template. 15.-17.(canceled)
 18. The non-transitory computer readable program storage unitof claim 1, wherein said method further comprises taking an action inresponse to said indicating, wherein said action is selected fromproviding a warning of said brain state change, logging a time of saidbrain state change, computing one or more brain state change indices,logging one or more computed brain state change indices, and providingat least one treatment of said brain state change.
 19. (canceled) 20.The non-transitory computer readable program storage unit of claim 1,wherein said comparing further comprises comparing said shape data witha corresponding third reference shape template for said at least aportion of a heart beat complex of said patient, and wherein saidindicating is based upon a determination that said shape data fails tomatch said first reference shape template and a determination that saidshape data fails to match said third reference shape template.
 21. Thenon-transitory computer readable program storage unit of claim 1,wherein obtaining shape data comprises obtaining shape data relating toa shape of at least a portion of a plurality of heart beat complexes;wherein said comparing comprises comparing said shape of at least aportion of a plurality of heart beat complexes with said first referenceshape template; and wherein said indicating is based upon adetermination that said shape of at least a portion of said plurality ofheart beat complexes fails to match said first reference shape template.22.-37. (canceled)
 38. The non-transitory computer readable programstorage unit of claim 1, wherein obtaining shape data comprisesobtaining data relating to a shape of a plurality of portions of a heartbeat complex, and wherein comparing said shape data comprises comparingsaid shape of a plurality of portions of a heart beat complex with eachof a corresponding plurality of portions of said first reference shapetemplate.
 39. The non-transitory computer readable program storage unitof claim 1, wherein obtaining shape data comprises obtaining datarelating to the shape of a PQRST complex from a patient, and whereincomparing said shape data comprises comparing said shape a PQRST complexwith a PQRST reference shape template.
 40. The non-transitory computerreadable program storage unit of claim 1, wherein said comparingcomprises comparing said shape data with a plurality of reference shapetemplates, and wherein said indicating is based upon a determinationthat said shape data fails to match said plurality of reference shapetemplates.
 41. The non-transitory computer readable program storage unitof claim 1, wherein said first reference shape template is selected froma non-ictal template, an ictal template, and a post-ictal template. 42.A method for indicating an occurrence of an epileptic seizure,comprising: obtaining a time series of heart beat complex data from apatient; determining at least one heart beat complex shape parameter foreach of a plurality of heart beat complexes in said time series of heartbeat complex data; comparing said at least one heart beat complex shapeparameter for each of said plurality of heart beat complexes to a firstreference heart beat complex shape template; indicating an occurrence ofan epileptic seizure based upon a determination that said at least oneheart beat complex shape parameter for at least a portion of saidplurality of heart beat complexes fails to match said first referenceheart beat complex shape template.
 43. The method of claim 42, furthercomprising: in response to said indicating, performing at least onefurther action selected from providing a warning; logging at least oneof said indicating, the time of said indicating, and a seizure severitymetric; and initiating at least one therapy to treat said epilepticseizure.
 44. The method of claim 43, wherein said at least one therapyis selected from: applying an electrical signal to a neural structure ofthe patient, wherein said neural structure is selected from a portion ofa brain structure of the patient, a portion of a cranial nerve of apatient, a portion of a spinal cord of a patient, a portion of asympathetic nerve structure of the patient, a portion of aparasympathetic nerve structure of the patient, and a portion of aperipheral nerve of the patient; delivering a drug to a patient; andcooling a neural structure of a patient.
 45. The method of claim 42,wherein determining at least one heart beat complex shape parametercomprises determining a plurality of heart beat complex shape parametersdefining a shape of each of a plurality of heart beat complexes in saidtime series; and wherein said comparing comprises comparing saidplurality of heart beat complex shape parameters for each of saidplurality of complexes to a corresponding plurality of reference heartbeat complex shape parameters defining said first reference heart beatcomplex shape template.
 46. The method of claim 42, wherein said firstreference heart beat complex shape template is selected from one of anon-ictal heart beat complex template, an ictal heart beat complextemplate, and a post-ictal heart beat complex template.
 47. The methodof claim 42, wherein determining at least one heart beat complex shapeparameter comprises using ECG data to determine a heart beat complexshape for each of a plurality of heart beat complexes in said timeseries, and wherein said first reference heart beat complex shapetemplate comprises a reference non-ictal PQRST complex template.
 48. Themethod of claim 47, wherein said first reference non-ictal PQRST complextemplate is a composite PQRST complex template constructed from aplurality of non-ictal PQRST complexes of said patient.
 49. A method forindicating an occurrence of an epileptic seizure, comprising: obtaininga time series of heart beat complex data from a patient; determining atleast one heart beat complex shape parameter for each of a plurality ofheart beat complexes in a moving window comprising a portion of saidtime series; comparing said at least one heart beat complex shapeparameter for each of said plurality of heart beat complexes in saidmoving window to a first reference heart beat complex shape template;indicating an occurrence of an epileptic seizure based upon adetermination that a threshold fraction of said at least one heart beatcomplex shape parameters of said plurality of heart beat complexes insaid moving window fail to match said first reference heart beat complexshape template.